Senior Data Engineer
Summary
Senior Data Engineer at Lower, a mortgage fintech, designing and building the Snowflake/dbt data warehouse that powers reporting, analytics, and decision-making, with Looker and Domo BI layers on top. Owns complex pipelines end to end, mentors other engineers, and works hybrid from Columbus, OH or Austin, TX.
- Design and build major components of our warehouse in Snowflake - ingestion layer, transformation layer, and core schema domains - including work in legacy Redshift environments as we modernize.
- Lead the ingestion of our most complex datasets into the core schema, across a wide variety of source systems, connectors, data shares, SFTP transfers, APIs, and native integrations.
- Define modeling standards within your domain using dbt, and build curated models, marts, and reusable data assets that support reporting, analytics, operations, and executive decision-making.
- Own complex initiatives end-to-end: define scope and a technical plan before execution, then deliver durable, scalable solutions with minimal oversight.
- Solve ambiguous, multi-system technical problems from evolving requirements, and make design decisions that hold up as the business grows.
- Anticipate scalability constraints and downstream impacts, identify re-architecture needs proactively, and propose the fix.
- Set a high bar for code quality within your domain through thoughtful, consistent code review, drive improvements in testing, validation, and documentation practices, and keep architecture and modeling consistent across our repos.
- Support and troubleshoot business-critical pipelines and jobs in production, including incident response for the systems you own.
- Work directly with stakeholders across marketing, sales, operations, finance, product, technology, and mortgage operations - including analysts and BI teams in Looker and Domo - to translate what they need into trusted, consistent data models and metrics.
- Mentor junior and mid-level engineers through code review, pairing, and technical guidance, and influence modeling and architecture decisions within the team.
- High give a sh*t factor - You care about the quality of what you deliver, holding yourself to a high standard - and you do your best work alongside people who do the same.
- Same team, same mission - You collaborate generously and treat teammates and stakeholders as partners, not requesters. Everyone here is working toward the same ambition.
- A problem-solver at heart - You're drawn to hard, ambiguous challenges, and you're motivated by building solutions the business actually feels.
- Strong opinions, loosely held - You bring a real point of view and make the case for it - in a doc, a code review, or a room - and you update when the evidence says to. You care more about getting it right than being right.
- 5-8+ years in data engineering, analytics engineering, BI engineering, or a similar data-focused role, with demonstrated technical ownership and impact.
- Deep SQL expertise and extensive experience with large, complex, and messy datasets.
- A track record of designing and owning production pipelines and data models that other people built on top of - not just building to spec.
- Strong experience with a cloud data warehouse such as Snowflake, Redshift, BigQuery, or Databricks, including performance tuning and cost optimization.
- Substantial experience with dbt or a similar transformation framework, including designing reusable patterns and standards.
- Strong Python (or similar) for data processing, automation, and pipeline orchestration.
- Deep familiarity with data integration patterns: APIs, SFTP transfers, file-based ingestion, third-party connectors, data shares, and native platform integrations.
- Fluency with BI and analytics tools such as Looker, Domo, Tableau, or Power BI, including semantic layer design.
- Hands-on experience using AI-assisted development tools in real engineering work - coding, documentation, testing, code review, or automation.
- Comfort operating with limited direction: scoping your own work, sequencing it, and knowing when to pull others in.
- Experience mentoring or informally leading other engineers.
- Ability to communicate clearly with both technical and non-technical stakeholders, including leadership.
- Mortgage, lending, financial services, real estate, or fintech.
- Hands-on Snowflake, dbt, Looker, and/or Domo at scale.
- Claude Code, Cursor, GitHub Copilot, or similar AI-assisted development tools.
- Building with AI agents, workflow automation, or LLM-powered internal tools.
- Orchestration tools, cloud platforms, CI/CD, or modern data stack tooling.
- Designing data governance, data quality testing, or observability frameworks rather than following existing ones.
- Supporting executive reporting, operational analytics, marketing analytics, mortgage operations, or sales funnel reporting.
- Leading technical design reviews.
- Extended benefit offerings including medical/dental/vision, parental leave, life insurance, short- and long-term disability
- Paid holidays and paid time off
- 401K with company match
- Discount on home mortgage refinances or purchase