Senior Data Engineer

Overview

Build and maintain scalable data pipelines, warehouses and data models that power underwriting, document processing and borrower interactions at a mortgage lender, using AI-assisted tooling throughout the data engineering workflow.

What you'll do

• Design, implement and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources.
• Use AI-assisted development tools to accelerate pipeline authoring, generate transformation logic and automate boilerplate code.
• Build and optimize data warehouses and marts (Snowflake, BigQuery, or similar) for analytics, reporting and product use cases.
• Design, implement and maintain conceptual, logical and physical data models for scalable, consistent, high-quality datasets.
• Ingest data from APIs, SaaS platforms (CRM, financial data APIs) and internal systems into the core data platform.
• Build and maintain reliable connectors and ingestion frameworks that handle schema evolution, rate limits and error recovery.
• Implement validation, schema management and robust documentation to ensure data accuracy and compliance.
• Use AI tools to support data profiling, anomaly detection and automated documentation of data lineage and transformations.
• Use AI-assisted tools (code generation, intelligent autocomplete, automated testing) as a regular part of the data engineering workflow.
• Evaluate and integrate emerging AI tools and practices into the team's data development process.
• Build and support agentic workflows and multi-step automated processes that act on data in real time, including AI-powered data validation and enrichment.
• Apply AI-assisted analysis to debugging pipeline failures, optimizing query performance and identifying data quality issues.
• Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency.
• Set up logging, monitoring and alerting for data jobs to ensure reliability and fast incident response.
• Apply data security and privacy controls aligned with financial regulatory requirements, ensuring full traceability of every transformation.
• Provide clean, consistent datasets for analysts, product managers and operational teams.
• Collaborate closely with product managers, data scientists and full stack engineers to align data models with business needs.

What you'll need

• 5+ years in a data engineering or similar backend data-focused role.
• Strong SQL and Python development skills for data transformation and automation.
• Experience with modern ETL/ELT frameworks such as dbt.
• Proficiency with cloud platforms (AWS preferred) and serverless data services.
• Strong experience with data warehouse technologies (Snowflake preferred).
• Skilled in API integrations and ingestion from third-party systems.
• Proficient in data modeling (Kimball/Star schema, Data Vault).
• Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate data pipeline development, debugging or documentation.
• Proven track record of delivering production-grade data pipelines at scale.
• Experience implementing CI/CD practices for data workflows.
• Experience collaborating closely with product managers, data scientists and full stack engineers.
• Startup mindset: hands-on, resourceful and comfortable operating in a fast-paced environment.

Nice to have

• Experience building agentic workflows and orchestrating multi-step automated processes that act on data in real time.
• Familiarity with data engineering patterns and infrastructure required for AI-powered tools and automation platforms.
• Experience working with financial datasets and APIs in a high-compliance environment.
• Understanding of data privacy regulations such as GDPR and CCPA.
• Experience with prompt engineering for code generation, data transformation logic or building AI-powered data workflows.

Details

• Remote-first with flexible hours.
• Benefits: competitive salary, unlimited PTO, up to $2,000/year professional development budget, home office setup stipend.