Sr. Analytics Engineer

About the Role

Our client is seeking a Senior Data Analytics Engineer who blends strong software engineering fundamentals with modern analytics, self-service BI enablement, and applied AI collaboration. This role emphasizes senior-level ownership and systems thinking, treating analytics as production software rather than one-off reporting.

You will design and build scalable, well-engineered analytics products that empower business users to answer their own questions through governed, self-service BI platforms. AI is treated as a first-class collaborator and is integrated into development workflows to accelerate insight generation, improve productivity, and enhance documentation quality.

This is a software engineering role embedded in analytics, not a dashboard builder role, requiring strong engineering rigor including modular architecture, reusable components, observability, and technical debt management. You will work across the modern data stack (e.g., cloud data warehouses, transformation frameworks, semantic layers, and BI tools) while partnering closely with data scientists, engineers, and business stakeholders to deliver scalable analytics systems.

Key Responsibilities

Analytics Engineering & Software Development

• Design, build, and maintain production-grade analytics data models using software engineering best practices (modularity, testing, version control, CI/CD).
• Develop reusable analytics components (metrics, dimensions, semantic layers, dbt packages, macros) that support consistent, governed self-service BI.
• Write performant SQL and transformation code optimized for large-scale cloud data platforms.
• Apply engineering rigor to analytics codebases, including code reviews, documentation, observability, and technical debt management.

Self-Service BI Enablement

• Architect and maintain self-service BI frameworks that enable analysts and business users to explore data safely and confidently.
• Partner with stakeholders to translate business questions into robust metrics, dashboards, and exploratory data products.
• Define and enforce metric definitions, data contracts, and semantic layers to ensure consistency across reports and teams.
• Train and support business users in effective data exploration, interpretation, and responsible use of analytics tools.

AI-Enabled Analytics & Collaboration

• Leverage AI-assisted development tools (e.g., code copilots, SQL generation, documentation generation) to accelerate analytics engineering workflows.
• Develop and refine prompts for AI systems to generate SQL, Python, documentation, data quality checks, and analytical narratives.
• Critically evaluate, validate, and improve AI-generated outputs to ensure correctness, performance, and alignment with business context.
• Collaborate with data scientists and ML engineers to operationalize AI- and ML-ready datasets.
• Contribute to emerging best practices for human–AI collaboration in analytics and BI.

Cross-Functional Partnership

• Collaborate with data engineering, data science, product, finance, sales, and marketing partners to deliver business-driven analytics solutions.
• Gather and refine requirements, define success criteria, and iterate on analytics products based on stakeholder feedback.
• Communicate complex technical concepts clearly to non-technical audiences.

Required Skills & Experience

• Strong experience with modern analytics stacks, including cloud data warehouses, dbt, and BI tools.
• Strong preference for experience owning the full stack from ingestion to warehousing to ETL to analytics.
• Strongly desired: deep, hands-on experience with Looker, including LookML modeling, explores, derived tables, dashboard development, performance optimization, and governed semantic layer design.
• Solid software engineering fundamentals applied to analytics (Git, testing, code reviews, modular design).
• Advanced SQL skills and experience working with large, high-volume datasets.
• Experience designing and supporting self-service BI and governed metrics layers.
• Hands-on experience using AI tools for analytics development, including prompt-based code or insight generation.
• Ability to assess and validate AI-generated outputs for accuracy, performance, and business relevance.
• Experience integrating data from APIs and external systems.
• Strong communication skills and a demonstrated ability to simplify complex ideas for diverse audiences.

Education & Experience

• Bachelor’s or Master’s degree in computer science, data science, statistics, mathematics, information systems, or a related quantitative field.
• 4–5+ years of experience in analytics engineering, data engineering, data science, or a related role.
• 3+ years building dashboards, reports, or semantic layers in a BI platform.
• 2+ years maintaining analytics data models over time in a production environment.

Nice to Have

• Experience with Python for data analysis, automation, or ML workflows.
• Familiarity with customer data platforms and marketing or sales analytics domains.
• Experience defining analytics standards, templates, or internal analytics frameworks.
• Exposure to experimentation platforms, forecasting, or advanced statistical methods.
• Strong interest in the evolving role of AI in analytics, BI, and decision-making.

What Success Looks Like

• Business users can confidently self-serve answers using trusted metrics and well-designed analytics products.
• Analytics codebases are reliable, testable, and scalable—treated as long-lived software, not one-off analyses.
• AI tools measurably improve development velocity and documentation quality, with human oversight ensuring trust and correctness.
• Stakeholders view analytics as a strategic capability that accelerates better, faster decisions.