Senior Analytics Engineer at Forward Financing designing and optimizing data architecture and models. Collaborating with cross-functional teams to deliver AI-ready data insights.
Responsibilities
Design, develop, and optimize scalable dimensional data models and marts using dbt
Build and extend Forward's semantic layer and metrics standards so key business KPIs are defined once, governed clearly, and consumed consistently across dashboards, models, AI agents, and downstream products
Help deliver the data foundation that powers AI at Forward - contributing the high-quality models, metadata, and governance that make Snowflake Intelligence and other AI/agent use cases trustworthy and production-ready
Advance Forward's data centralization vision by collaborating across BI, Data Science, Data Engineering, and Product to consolidate sources of truth and eliminate fragmented business logic
Act as the primary liaison with the Data Science team to translate features into production-ready data variables required for training and validating predictive models
Collaborate closely with the Data Engineering team to ensure the robust, reliable deployment and orchestration of data variables that feed production machine learning models
Partner with the Core Technology teams on internal application schema changes and data migrations, ensuring rigorous data accuracy validation and maintaining minimal disruption to downstream analytical models
Collaborate with the DevOps team to monitor, maintain, and contribute to our independent, high-performance streaming pipelines tailored for real-time analytics use cases
Elevate team code quality by serving as a technical reviewer for Analytics team pull requests, coaching junior members, and enforcing strict adherence to internal standards for style, maintainability, performance, and modern best practices
Evaluate, integrate, and operationalize high-value third-party data sources to strategically enrich and expand the overall data ecosystem
Champion data governance and quality across the data platform - including automated dbt tests, thorough data cataloging, lineage, and compliance with security and regulatory standards - so stakeholders *and* AI systems can both rely on the numbers.
Requirements
4+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence
2+ years of hands-on experience developing and maintaining production-level data transformation pipelines using dbt or equivalent scripting experience in Python
2+ years of experience with a cloud-based data warehouse such as Snowflake or Redshift
Advanced proficiency in SQL and dimensional data modeling, with a track record of building models that are durable, well-tested, and easy for downstream consumers to use
Demonstrated strong ability to quickly translate complex business needs and ambiguous requirements into robust, scalable technical data solutions.
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