Senior Analytics Engineer

About the role

We're adding a dedicated engineer to our Operations Analytics team to close the gap 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. You'll ensure these data products are accurate, reliable, well-documented, and prioritized around business needs. This is a hands-on engineering role with an analyst's instincts and a communicator's temperament. You will act as the technical translator enabling Analytics to move fast while staying aligned with Data Engineering's platform, standards, and guardrails.

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, ensuring consistent definitions across the team.
  • Turn business questions into data products—translate Analytics and Marketing needs 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 checks that catch problems first—freshness, volume, schema, and business-rule validation to prevent issues from reaching reports, customers, or campaigns.
  • Take ownership of critical recurring data products—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.
  • Validate business accuracy—stand behind the numbers by understanding both the data and the business.
• 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 to build confidently on your models.
  • Communicate exceptionally—explain technical trade-offs clearly to non-technical stakeholders 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.

Requirements

• 5+ years combined experience across data engineering and analytics—strong in both disciplines, not one with passing knowledge of the other.
• Strong analytical judgment—explore data, determine its meaning, and turn ambiguous business questions into clear, defensible answers.
• 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 experience with 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—proven ability to translate 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.

Work Environment

• Office environment.
• Occasional travel may be required.

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 up to 25 pounds.