Senior Analytics Engineer
About The Position
We're adding a dedicated engineer to our Operations Analytics team to close a specific gap: the space between raw, platform-grade data and the trusted, business-ready datasets our analysts and marketers rely on. You'll build and own the analytics data layer — curated models, marts, and metrics, including rewriting and migrating existing SQL Server models into Snowflake — on top of the base tables maintained by the IT Data Engineering team, and you'll make sure those data products are accurate, reliable, well documented, and prioritized around what the business actually needs. This is a hands-on engineering role with an analyst's instincts and a communicator's temperament. You will not work in isolation: you're the technical translator who lets Analytics move fast and keeps us aligned with Data Engineering's platform, standards, and guardrails.
Requirements
- 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.
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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
