Note: The job is a remote job and is open to candidates in USA. Forward Financing is a financial technology company based in Boston, Massachusetts, dedicated to unlocking capital for small businesses across America. As a Senior Analytics Engineer, you will design, build, and maintain data models and collaborate with various teams to ensure high-quality data products that support AI-driven experiences and informed decision-making.
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
Skills
- 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
- Hands-on experience with Python for data scripting, automation, or predictive modeling support
- Experience designing or contributing to a semantic layer / metrics layer that serves as an organizational source of truth
- Experience building data products that support AI, LLM, or agent-based applications (e.g., Snowflake Intelligence, Cortex, RAG over warehouse data, or similar)
- Familiarity with streaming/CDC pipelines (Streamkap, Fivetran, Kafka) and real-time analytics use cases
- Experience in fintech, lending, or another regulated, data-intensive industry
Benefits
- Annual Target Bonus: 10%
Company Overview