Senior Analytics Engineer

Essential Duties and Responsibilities: Build the analytics data layer Model trusted, reusable datasets — design and build curated tables, marts, and a semantic/metrics layer on top of the platform's base tables, so the whole team reports on the same definitions. Turn business questions into data products — translate what Analytics and Marketing need into performant, well-tested SQL and documented datasets. Write engineering-grade code — version-controlled, peer-reviewed, and built to the team's standards, not one-off scripts. Migrate & modernize — rewrite and migrate existing SQL Server data warehouse/data store models, stored procedures, and jobs into Snowflake, following the team's patterns and standards. Own reliability, quality & accuracy Build the checks that catch problems first — freshness, volume, schema, and business-rule validation so issues are caught  before  they reach reports, customers, or campaigns. Take ownership of critical recurring data products — the scheduled jobs, stored procedures, and models behind high-stakes processes (e.g., incentive-compensation calculations) — treating them as products with clear owners, SLAs, monitoring, alerting, and runbooks, built to run reliably rather than patched. Validate business accuracy — because you understand the data and the business, you can stand behind the numbers. Bridge, communicate & enable Be the translator — represent Analytics' priorities to Data Engineering and bring engineering discipline back to Analytics; speak both languages fluently. Make data self-serve and trusted — document datasets, definitions, and lineage; enable and coach analysts so they can build confidently on your models. Communicate exceptionally — explain technical trade-offs to non-technical stakeholders clearly, and keep partners informed on status, risks, and timelines. Mind performance and cost — write efficient queries and manage warehouse usage within the platform's cost and governance guardrails. Education and/or Experience: 5+ years combined experience across data engineering and analytics — genuinely strong on both sides, not one with a passing knowledge of the other. Strong analytical judgment — you can explore data, figure out what it actually means, and turn ambiguous business questions into clear, defensible answers, not just build to spec. Advanced SQL and hands-on data modeling (dimensional models, marts, semantic layers). Experience with a cloud data warehouse (Snowflake preferred) and a transformation framework (e.g., dbt or equivalent). SQL Server / T-SQL and Python — hands-on across an existing SQL Server data warehouse/data store, including migrating and rewriting its objects into Snowflake. Comfort with orchestration (e.g., Airflow) and version control / CI/CD (Git; Azure DevOps a plus). Experience building or supporting BI/reporting (Power BI preferred). Exceptional communication and stakeholder partnership — a track record of translating between business and technical teams. Demonstrated ownership of data quality and reliability (monitoring, validation, SLAs). Financial services / consumer lending domain experience; comfort with regulated data (GLBA, PII handling). Experience embedded in a business team while partnering with a central platform/DE group. Experience migrating SQL Server / T-SQL workloads to Snowflake at scale. Familiarity with data catalog / lineage and cost-management practices on Snowflake. A habit of documentation and enablement — you make others better with data. Physical Demands: Must be able to constantly remain in a stationary position. Constantly operates a computer and other office productivity machinery, such as a calculator, copy machine, and computer printer. Occasionally may require light lifting to 25 pounds. Work Environment: Office environment. Occasional travel may be required. This job description reflects management’s assignment of essential functions; and nothing in this herein restricts management’s right to assign or reassign duties and responsibilities to this job at any time.