logoAiPathly

Data & Analytics BI Development Specialist Senior

first image

Overview

A Senior Business Intelligence (BI) Development Specialist plays a crucial role in transforming raw data into actionable insights that drive business decisions. This overview highlights their key responsibilities, required skills, and role within an organization.

Responsibilities

  • Design, develop, and maintain BI solutions, including integration with databases and data warehouses
  • Create interactive dashboards, reports, and visualizations using tools like Tableau, Power BI, or QlikView
  • Manage data modeling and database performance, security, and integrity
  • Oversee data extraction, transformation, and loading (ETL) processes
  • Collaborate with stakeholders to understand data needs and translate business requirements into technical solutions
  • Manage multiple projects simultaneously, applying Agile methodologies
  • Ensure data quality, implement governance policies, and maintain data integrity
  • Provide technical documentation and troubleshooting support

Skills and Proficiencies

  • Expertise in database management systems, BI technologies, and programming languages (e.g., SQL, Python, R)
  • Strong data analysis and visualization skills
  • Problem-solving and critical thinking abilities
  • Effective communication skills and business acumen
  • Project management and leadership capabilities

Role Within the Organization

  • Contribute to strategic decision-making by enhancing BI systems
  • Collaborate across various teams, including business analysts, data scientists, and IT professionals
  • Drive data-driven decision-making and long-term business strategy development In summary, a Senior BI Development Specialist is essential for transforming data into actionable insights, maintaining BI systems, and ensuring data-driven decision-making throughout the organization. Their role requires a blend of technical, analytical, and business skills to drive growth and efficiency.

Core Responsibilities

A Senior Data & Analytics BI Development Specialist has a wide range of core responsibilities that are critical to the success of an organization's business intelligence initiatives:

1. BI Solution Design and Implementation

  • Develop and implement comprehensive business intelligence solutions
  • Design and build BI reports, dashboards, and data visualizations using tools like Power BI, SAP Business Warehouse, and Business Objects

2. Data Architecture and Management

  • Design, modify, and maintain database architecture and schemas
  • Optimize database design and performance
  • Manage data warehouses and operational databases, both on-premise and in cloud environments (e.g., Azure, AWS, Snowflake)

3. ETL/ELT Development and Data Integration

  • Analyze source-to-target mappings and develop ETL/ELT specifications
  • Perform ETL/ELT development, deployment, and monitoring for enterprise data systems

4. Performance Optimization and Troubleshooting

  • Optimize data queries and resolve performance issues
  • Troubleshoot data issues, ETL/ELT processes, and reports to ensure uninterrupted access to enterprise database systems

5. Collaboration and Technical Leadership

  • Work with cross-functional teams to resolve complex systems and data flow issues
  • Provide technical leadership and mentoring to junior team members
  • Act as a top-level contributor on complex projects

6. Data Security and Compliance

  • Ensure system security and controls
  • Maintain appropriate processes for user access
  • Participate in master data management to ensure data quality, integrity, and security

7. Reporting and Analytics Support

  • Prepare test strategies, scripts, and documentation
  • Support the delivery of reporting, ad hoc, and analytics requests
  • Perform analysis, design, development, validation, and implementation of reports

8. Continuous Improvement and Training

  • Stay updated on new tools, methods, and techniques in data technologies
  • Drive workshops or training sessions to enhance knowledge of data, reporting, and visualization tools among business users

9. Project Management and Documentation

  • Establish and manage BI technical processes to ensure timely delivery of reports and dashboards
  • Support and maintain project documentation, including business requirements, technical design, and mapping documents These comprehensive responsibilities underscore the critical role of a Senior Data & Analytics BI Development Specialist in designing, implementing, and maintaining robust business intelligence solutions that drive organizational success.

Requirements

To excel as a Senior Data & Analytics Business Intelligence (BI) Development Specialist, candidates should possess a combination of technical expertise, analytical skills, and business acumen. Here are the key requirements:

Technical Skills

  • Database Management and SQL: Proficiency in complex SQL queries, database design, and optimization
  • Data Visualization Tools: Expertise in Tableau, Power BI, or QlikView for creating interactive dashboards
  • Cloud Platforms: Familiarity with AWS, Microsoft Azure, or Google Cloud Platform
  • ETL Processes: Experience with ETL tools like Informatica, Talend, or Microsoft SSIS
  • Programming Languages: Proficiency in Python, R, and SQL

Data Analysis and Interpretation

  • Ability to analyze large datasets to identify trends, patterns, and correlations
  • Develop and apply statistical models for complex data relationships
  • Identify and track key performance indicators (KPIs) and metrics

Business and Analytical Skills

  • Strong business acumen and understanding of industry-specific metrics
  • Problem-solving and critical thinking skills
  • Excellent communication skills for presenting insights to non-technical stakeholders

Project Management and Leadership

  • Experience managing multiple projects using Agile methodologies
  • Team leadership and mentoring abilities
  • Developing and implementing best practices for data analysis and reporting

Additional Responsibilities

  • Data collection and management
  • Business process improvement
  • Strategic planning and forecasting

Education and Experience

  • Bachelor's Degree in Computer Science, Engineering, Information Systems, or related field
  • Minimum 7 years of BI-related experience, with 4+ years as an expert-level BI developer

Key Attributes

  • Attention to detail and commitment to data quality
  • Ability to work independently and as part of a team
  • Adaptability to new technologies and methodologies
  • Strong problem-solving and analytical thinking skills This comprehensive skill set enables a Senior Data & Analytics BI Development Specialist to drive data-informed decision-making and contribute significantly to an organization's success through effective BI solutions.

Career Development

Developing a successful career as a Senior Business Intelligence (BI) Analyst or Data & Analytics BI Development Specialist requires strategic planning and continuous growth. Here are key areas to focus on:

Educational Foundation

  • Bachelor's degree in computer science, information systems, business administration, statistics, or data science is typically required.
  • A master's degree in data analytics, business analytics, or related fields can provide a competitive edge.

Professional Certifications

  • Certifications demonstrate expertise and commitment to the field:
    • Certified Business Intelligence Professional (CBIP)
    • Microsoft Certified: Data Analyst Associate
    • Tableau Desktop Specialist

Skill Development

  • Technical skills:
    • Data analysis and visualization
    • SQL and database management
    • Data modeling and warehousing
    • Tools: Power BI, Tableau, Excel
    • Agile methodologies and project management
    • Machine learning and predictive modeling (for advanced roles)

Work Experience

  • Start with entry-level roles like data analyst or business analyst.
  • Senior roles typically require 7+ years of BI-related experience, including 5+ years of SQL expertise.

Business Acumen and Soft Skills

  • Develop deep understanding of industry-specific metrics and KPIs.
  • Enhance ability to communicate complex data insights.
  • Build strong problem-solving, critical thinking, and collaboration skills.

Career Progression

  • Potential paths include BI Manager, Director, or specialized roles in data science or analytics strategy.
  • Consider consulting or freelancing opportunities.

Continuous Learning

  • Stay updated on new tools, techniques, and technologies in the rapidly evolving BI field.

Specialization Options

  • Consider specializing in:
    • Data Science: Machine learning and advanced statistical analysis
    • Data Engineering: Data warehousing and big data technologies
    • Analytics Strategy: Developing data-driven organizational strategies By focusing on these areas, you can chart a successful career path in Business Intelligence, driving business growth through data-driven insights.

second image

Market Demand

The demand for Data and Analytics professionals, particularly in Business Intelligence (BI) development and senior roles, continues to grow rapidly across various industries.

Industry Demand

  • High demand across finance, healthcare, e-commerce, marketing, and technology sectors.
  • Organizations increasingly rely on data-driven insights for competitive advantage.

Job Roles and Growth

  • Data Analyst: Collaborates with cross-functional teams to ensure data accuracy and provide recommendations.
  • Senior Business Intelligence Analyst: Transforms complex data into actionable insights, driving business growth.
  • BI Developer: Ensures data accuracy, optimizes performance, and provides insights through interactive dashboards.
  • Data Scientist: Translates complex findings into actionable recommendations and refines analytical models.
  • Surge in demand for BI roles due to emphasis on data-driven decision-making.
  • Salaries have increased by 10% over the last five years, with continued growth expected.

Industry-Specific Opportunities

  • Finance and Banking: Risk management and investment banking.
  • Healthcare: Improving patient outcomes and reducing costs.
  • E-commerce and Retail: Analyzing customer behavior and optimizing supply chains.

Job Market Statistics

  • Projected job growth rate for business intelligence analysts: 21% from 2018-2028 (Zippia).
  • Nearly 20% of global data analytics jobs are in the business intelligence sector.

Skills and Requirements

  • Technical skills: SQL, data visualization tools (Tableau, Power BI)
  • Strong analytical and communication skills
  • Continuous learning and staying updated with industry trends The demand for BI development specialists and senior data analytics professionals is expected to remain strong as data-driven strategies become increasingly crucial for organizational success.

Salary Ranges (US Market, 2024)

Senior Business Intelligence (BI) Development specialists in the US market can expect competitive compensation packages, with salaries varying based on experience, location, and industry.

Average Salary

  • The average annual salary for a Senior Business Intelligence Developer ranges from $112,266 to $172,000, depending on the source and specific role.

Salary Ranges

  • Broader range: $99,759 to $235,000 per year
  • Top earners (top 10%): Over $218,000
  • Most professionals: Between $88,371 and $140,432

Factors Influencing Salary

  1. Experience:
    • 7-10 years: $110,000 - $130,000
    • 10+ years: Up to $160,000 or more
  2. Industry: Finance and healthcare often offer higher salaries
  3. Geographic location: Varies by cost of living and local demand
  4. Additional compensation: Bonuses, stocks, and benefits can increase total package to up to $229,000

Skills and Qualifications Impacting Salary

  • Technical proficiency: SQL, Oracle, PL/SQL
  • Experience with BI software platforms and data analytics tools
  • Educational background: Graduate or postgraduate degree in a related field
  • Certifications: Microsoft Certified: Data Analyst Associate, etc.

Key Takeaways

  • Salaries for Senior BI Development specialists are competitive and expected to grow
  • Experience and specialized skills significantly impact earning potential
  • Consider total compensation package, including benefits and bonuses
  • Continuous skill development and certifications can lead to higher earnings These figures provide a comprehensive view of salary expectations for Senior Business Intelligence Development specialists in the US market for 2024, highlighting the potential for lucrative careers in this field.

The Data & Analytics BI Development landscape is rapidly evolving, with several key trends shaping the industry in 2025 and beyond:

  1. Data Integration and Quality: Organizations are prioritizing the elimination of data silos and improving data quality to enhance accessibility and reliability of information.
  2. Real-Time Data Streaming: Implementing real-time data streaming capabilities is becoming crucial for quick responses to market changes and supporting legacy data architecture transformation.
  3. Advanced AI and Machine Learning: Generative AI (GenAI) and Explainable AI (XAI) are revolutionizing BI, enhancing user experience through interactive data exploration and ensuring transparency in AI-driven systems.
  4. Data Enrichment and Augmented Analytics: Expanding data catalogs with synthetic and third-party data, while leveraging AI-powered augmented analytics to automate tasks like data preparation and visualization.
  5. Self-Service and Collaborative BI: The democratization of data analysis through self-service BI tools and the enhancement of collaborative BI driven by GenAI and NLP.
  6. Data Governance and Compliance: Increasing focus on ensuring data accuracy, security, and compliance with regulations like GDPR.
  7. Customized BI Solutions: Businesses are seeking tailored BI solutions that address their specific needs while emphasizing data security and discovery.
  8. Horizontal Integration and Data Mesh: Push for integrating data analytics across more processes and company divisions, supported by technologies like data mesh and reverse ETL. These trends underscore the need for continuous innovation, improved data governance, and the integration of advanced technologies in the BI and analytics field.

Essential Soft Skills

For Senior Data & Analytics BI Development Specialists, the following soft skills are crucial for success:

  1. Communication: Ability to translate complex data insights into understandable language for non-technical stakeholders, including clear report writing and effective presentations.
  2. Collaboration: Working effectively with cross-functional teams, adapting to different communication styles and perspectives.
  3. Strategic Thinking: Aligning data insights with company's strategic goals and understanding the broader business context.
  4. Problem-Solving: Identifying and addressing issues in BI projects, from data quality problems to integration challenges.
  5. Time Management and Organization: Managing multiple projects, meeting deadlines, and prioritizing tasks effectively.
  6. Critical Thinking and Curiosity: Analyzing data, interpreting results, and exploring data to uncover valuable insights and trends.
  7. Attention to Detail: Ensuring accuracy and comprehensiveness in analytics results and catching data issues.
  8. Business Acumen: Understanding industry-specific metrics, benchmarks, and KPIs to develop insights that drive business outcomes. By developing these soft skills, professionals can effectively collaborate with teams, communicate insights clearly, and drive data-driven strategies that align with organizational goals.

Best Practices

To excel as a Data & Analytics BI Development Specialist, consider the following best practices:

  1. Align with Business Goals: Define clear BI objectives that support overall organizational strategy and identify key performance indicators (KPIs).
  2. Ensure Data Quality: Regularly assess, clean, validate, and enrich data to maintain accuracy and relevance.
  3. Foster a Data-Driven Culture: Promote the value of data-backed decisions and encourage continuous learning across the organization.
  4. Implement Self-Service Analytics: Empower teams with user-friendly BI tools to enhance collaboration and accelerate decision-making.
  5. Establish Data Governance: Implement robust practices to ensure data security, privacy, and compliance with relevant regulations.
  6. Leverage Appropriate Tools: Utilize BI technologies that align with organizational needs and data complexity.
  7. Master Data Integration: Oversee effective ETL processes and integrate data from various sources for comprehensive business insights.
  8. Excel in Visualization and Reporting: Develop clear, actionable dashboards and reports that facilitate quick understanding of insights.
  9. Collaborate Effectively: Work closely with stakeholders to understand data needs and translate business requirements into technical solutions.
  10. Continuous Improvement: Regularly review and update BI strategies to align with changing business conditions and emerging technologies.
  11. Develop Technical and Analytical Skills: Maintain proficiency in database management, SQL, data modeling, and programming languages.
  12. Enhance Business Acumen: Understand industry-specific metrics and effectively manage projects using methodologies like Agile.
  13. Embrace Continuous Learning: Stay updated on industry trends, technologies, and best practices through ongoing professional development. By adhering to these best practices, BI professionals can significantly contribute to organizational success and growth through data-driven decision-making and insights.

Common Challenges

Data & Analytics BI Development Specialists often face several challenges in implementing effective BI solutions:

  1. Data Integration: Combining data from diverse sources, including databases, spreadsheets, cloud applications, and IoT devices.
  2. Data Quality Issues: Ensuring accuracy, consistency, and completeness of data through validation processes and cleansing techniques.
  3. Breaking Down Data Silos: Overcoming isolated data storage within specific teams or systems to enable seamless information flow.
  4. Complex Analytics and Testing: Managing time-consuming manual testing of SQL logic, ETL processes, and data warehouse systems.
  5. Low User Adoption: Addressing resistance to change and ensuring BI tools are user-friendly to encourage widespread use.
  6. Ineffective Data Visualization: Designing intuitive, interactive dashboards that deliver actionable insights to decision-makers.
  7. Creating a Data-Driven Culture: Shifting organizational mindset to value data-driven decision-making through training and transparency.
  8. Skill Gaps: Addressing the shortage of professionals who can interpret complex data sets and design meaningful visualizations.
  9. Managing Self-Service BI: Balancing user empowerment with proper data governance and security protections.
  10. Data Privacy and Security: Implementing robust processes to protect customer data and comply with privacy regulations.
  11. Technological Complexity: Integrating various BI and analytics technologies while managing knowledge silos and skills gaps. Overcoming these challenges requires a multifaceted approach, including strategic planning, investment in training, effective data governance, and the implementation of integrated, user-friendly BI systems. By addressing these issues, organizations can unlock the full potential of their data assets and drive informed decision-making across all levels.

More Careers

Manager Data Governance

Manager Data Governance

A Data Governance Manager plays a pivotal role in ensuring the effective management, protection, and quality of an organization's data assets. This role is crucial in today's data-driven business environment, where proper data management can significantly impact an organization's success and compliance. Key Responsibilities: - Establish and implement a comprehensive data governance framework - Improve data quality and protect sensitive information - Define roles and responsibilities for data stewardship - Act as a liaison between business, functional areas, and technology teams - Develop and maintain an inventory of enterprise information - Define and monitor performance metrics and compliance - Manage data-related risks and promote data literacy - Provide strategic oversight and align data governance with organizational objectives Role Levels: 1. Data Governance Manager: Focuses on practical implementation and data quality improvement 2. Lead Data Governance Manager: Handles complex governance tasks and coordinates with data owners 3. Head of Data Governance: Provides strategic oversight and acts as a senior advisor The importance of this role lies in its ability to ensure data trustworthiness, consistency, and efficient use. A strong data governance program helps organizations maintain high data quality, reduce management costs, increase data accessibility, and mitigate risks associated with data misuse or errors. By effectively managing an organization's data assets, Data Governance Managers contribute significantly to informed decision-making, operational efficiency, and regulatory compliance.

Lead ML Performance Engineer

Lead ML Performance Engineer

The role of a Lead Machine Learning Performance Engineer is a senior position that combines advanced technical expertise in machine learning with strong leadership and project management skills. This role is critical in optimizing and scaling machine learning models and systems across various industries. ### Key Responsibilities - **Performance Optimization**: Analyze and enhance the performance of machine learning models and systems, identifying bottlenecks and developing strategies for model tuning and efficient resource usage. - **Cross-functional Collaboration**: Work closely with various teams, including feature, product, hardware, and software teams, to align machine learning initiatives with business objectives and technical requirements. - **Leadership and Mentoring**: Lead and manage teams of machine learning engineers, providing guidance, mentoring, and overseeing the development and deployment of machine learning models. - **Technical Expertise**: Maintain a strong understanding of machine learning algorithms, deep learning architectures, and hardware optimization techniques. ### Required Skills and Qualifications - Advanced knowledge of machine learning algorithms and deep learning frameworks (e.g., TensorFlow, PyTorch) - Proficiency in programming languages such as Python, R, or Java - Experience with cloud platforms (AWS, Google Cloud, Azure) - Strong leadership and team management skills - Excellent communication abilities and project management experience - Typically, a Bachelor's degree in Computer Science, Data Science, or a related field, with a Master's or Ph.D. often preferred ### Tools and Technologies - Deep learning frameworks: TensorFlow, PyTorch, Hugging Face - Performance optimization tools: GPU profiling tools, Metal, CUDA/Triton - Project management tools: Jira, Trello, Asana, Git, GitHub, GitLab ### Industry Outlook The demand for Lead ML Performance Engineers is growing rapidly across various sectors, including technology, finance, healthcare, retail, and manufacturing. This growth is driven by the increasing adoption of AI and machine learning technologies and the need for efficient, scalable solutions. According to the U.S. Bureau of Labor Statistics, employment for related roles is projected to grow significantly faster than the average for all occupations, indicating a promising career path for those with the right skills and expertise.

Manager Data Strategy

Manager Data Strategy

A data strategy is a comprehensive plan that outlines how an organization manages, utilizes, and derives value from its data assets. Key components of an effective data strategy include: 1. Alignment with Business Goals: Ensure the strategy supports overall business objectives. 2. Clear Objectives: Define specific, measurable goals for data management and utilization. 3. Current State Assessment: Evaluate existing data assets, processes, and capabilities. 4. Data Management: Establish processes for collecting, organizing, protecting, and sharing data. 5. Data Architecture: Design scalable systems for efficient data flow and storage. 6. Data Governance: Implement policies for ethics, privacy, security, and compliance. 7. Analytics Capabilities: Define methods for turning raw data into actionable insights. 8. Cross-Functional Collaboration: Involve stakeholders from various departments to break down silos. 9. Implementation Roadmap: Create a detailed plan with milestones and timelines. 10. Documentation and Maintenance: Record all aspects of the strategy for future reference. Benefits of a robust data strategy include: - Improved operational efficiency and decision-making - Enhanced regulatory compliance and data security - Support for advanced applications like machine learning and AI - Creation of a data-driven organizational culture A well-executed data strategy is crucial for organizations seeking to leverage their data assets effectively in today's data-centric business environment.

Machine Learning Advocate Engineer

Machine Learning Advocate Engineer

The role of a Machine Learning Advocate Engineer is a multifaceted position that combines technical expertise, community engagement, and advocacy within the AI industry. This role is crucial for companies looking to promote their AI and machine learning technologies while fostering a strong developer community. Key aspects of the Machine Learning Advocate Engineer role include: 1. Technical Expertise: - Strong skills in machine learning, particularly in areas such as computer vision and natural language processing - Proficiency in programming languages like Python, R, or Java - Familiarity with machine learning frameworks such as TensorFlow, Keras, or PyTorch 2. Community Engagement and Advocacy: - Creating, fostering, and growing developer communities around the company's products and technologies - Organizing and co-leading events to promote adoption of the company's platforms - Engaging with technical communities through various channels 3. Content Creation and Education: - Developing resources such as tutorials, blog posts, and workshops - Providing training and support for developers using the company's AI and ML products 4. Feedback and Product Improvement: - Collecting and relaying feedback from the developer community to internal teams - Contributing to product development by representing user needs 5. Collaboration with Internal Teams: - Working closely with Open Source, Science, and Product teams - Driving innovation and enhancing the developer experience 6. Representation and Leadership: - Serving as a technical representative for the company within the ML ecosystem - Demonstrating strong communication and leadership skills 7. Additional Skills: - Problem-solving and adaptability - Experience with cloud platforms and project management tools - Strong collaboration and communication abilities In summary, a Machine Learning Advocate Engineer plays a vital role in bridging the gap between technical development and community engagement, driving the adoption and improvement of AI and ML technologies while representing the company's interests in the broader AI ecosystem.