Data Engineer

Baselayer•San Francisco, CA

2d•Hybrid

About The Position

Baselayer is building the most comprehensive, accurate, and continuously-current identity graph of US businesses — fusing public records, IRS data, sanctions lists, web signals, and fraud telemetry from thousands of financial institutions into a single graph that resolves any business in milliseconds. None of that works without world-class data infrastructure. We’re hiring a Data Engineer to help build and run the pipelines and models that turn messy, heterogeneous data into trustworthy, production-grade signal. You’ll write real production code in your first weeks, own pipelines end to end, and learn alongside senior data and ML engineers who will invest in your growth. This is a role for an early-career engineer who wants to be close to the action: feeding the models, not just cleaning up after them.

Requirements

1+ years of experience in data engineering, working with Python, SQL, and cloud-native data platforms
Experience building and maintaining ETL/ELT pipelines in a production environment
Working knowledge of modern data stack tooling (e.g. Dataflow, Spark, Airflow or equivalents)
Hands-on experience with cloud data warehouses or lakes (e.g. BigQuery, Snowflake, or equivalents)
Solid data modeling fundamentals and real care for data integrity and reliability
Comfort with both structured and unstructured data, and a feel for what clean, scalable architecture looks like

Nice To Haves

Curiosity about AI/ML infrastructure and a desire to be close to the models, not just the cleanup after them
Experience with streaming or real-time data systems (e.g. Kafka, Pub/Sub)
Exposure to KYC/KYB, fraud, risk, or underwriting data, and the ethical care that sensitive information demands
GCP experience (BigQuery, Cloud Run, Dataflow, Pub/Sub)
You care deeply about data quality and trust, and build systems others can rely on
You’ve worked without a playbook before, and you take direct feedback well and act on it fast

Responsibilities

Build and maintain ETL/ELT pipelines that ingest and normalize public records, web signals, and fraud telemetry from dozens of sources
Develop data models and transformation layers (Dataflow, Spark, Airflow) that power fraud detection, KYB, and customer-facing APIs
Implement data quality checks, observability tooling, and alerting so problems surface before customers see them
Tune pipelines and queries for performance, freshness, and cost in our cloud data warehouse
Work with data scientists, ML engineers, and product to make clean, well-modeled data available for entity resolution and scoring
Help ensure pipelines meet security and regulatory standards for sensitive data (SOC 2, GDPR, KYC/KYB)
Document what you build and translate between technical and non-technical stakeholders so the rest of the team moves faster