IN THIS ROLE, YOU’LL GET TO
• Act as a partner and consultant to engineering managers and leaders: translate
stakeholder and engineering questions into measurable metrics/information such
as developer velocity, cost efficiency, and code quality, and keep those definitions
consistent across reports.
• Design, build, and maintain ETL/ELT transformations, including Spark/Python
workflows and warehouse models, so data is accurate, timely, tested, and fit for
reporting. As a Senior, review others’ pipeline and metric work, raise delivery
standards, and unblock the team.
• Analyze large datasets with SQL and Python to support engineering and operational
decisions.
• Design and maintain dashboards and visual reports so engineering managers and
leaders can explore metrics (for example developer velocity, cost efficiency, and
code quality) without a ticket for every chart.
• Improve data reliability through validation, quality checks, observability, production
troubleshooting, and query performance tuning. Own published metrics: explain
filters and grain, flag numbers that should not be trusted, and recommend next
steps.
• Use AI tools (for example Cursor, Claude, or similar) to accelerate SQL
development, testing, refactoring, analysis, pipeline work, and documentation, and
reject unsafe or unverified output before it reaches production or stakeholders.