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.
Essential Duties and Responsibilities:
Build the analytics data layer
- Model trusted, reusable datasetsdesign 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 productstranslate what Analytics and Marketing need into performant, well-tested SQL and documented datasets.
- Write engineering-grade codeversion-controlled, peer-reviewed, and built to the team's standards, not one-off scripts.
- Migrate & modernizerewrite 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 firstfreshness, volume, schema, and business-rule validation so issues are caughtbeforethey reach reports, customers, or campaigns.
- Take ownership of critical recurring data productsthe scheduled jobs, stored procedures, and models behind high-stakes processes (e.g., incentive-compensation calculations) 6reating them as products with clear owners, SLAs, monitoring, alerting, and runbooks, built to run reliably rather than patched.
- Validate business accuracybecause you understand the data and the business, you can stand behind the numbers.
Bridge, communicate & enable
- Be the translatorrepresent Analytics' priorities to Data Engineering and bring engineering discipline back to Analytics; speak both languages fluently.
- Make data self-serve and trusteddocument datasets, definitions, and lineage; enable and coach analysts so they can build confidently on your models.
- Communicate exceptionallyexplain technical trade-offs to non-technical stakeholders clearly, and keep partners informed on status, risks, and timelines.
- Mind performance and costwrite efficient queries and manage warehouse usage within the platform's cost and governance guardrails.
Education and/or Experience:
- 5+ yearscombined experience across data engineering and analytics 6enuinely strong on both sides, not one with a passing knowledge of the other.
- Strong analytical judgmentyou can explore data, figure out what it actually means, and turn ambiguous business questions into clear, defensible answers, not just build to spec.
- Advanced SQLand hands-ondata modeling(dimensional models, marts, semantic layers).
- Experience with acloud data warehouse(Snowflake preferred) and atransformation framework(e.g., dbt or equivalent).
- SQL Server / T-SQL and Pythonhands-on across an existing SQL Server data warehouse/data store, including migrating and rewriting its objects into Snowflake.
- Comfort withorchestration(e.g., Airflow) andversion control / CI/CD(Git; Azure DevOps a plus).
- Experience building or supportingBI/reporting(Power BI preferred).
- Exceptional communicationand stakeholder partnership 6 a track record of translating between business and technical teams.
- Demonstrated ownership ofdata 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 6 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.
