Data Engineer

Baselayer•San Francisco, CA

13d•$122,000 - $167,000•Hybrid

About The Position

We are looking for a Data Engineer to build and scale Baselayer’s data infrastructure. You will own the pipelines and data systems that power analytics, reporting, and machine learning across the company, with a focus on reliability, performance, and data quality. This role is hands-on and highly cross-functional. You will work closely with Product and Engineering to ensure data is accessible, trusted, and delivered in a way that supports product capabilities in a regulated environment.

Requirements

1 to 3 years of experience in data engineering, analytics engineering, or backend engineering with significant data pipeline ownership
Strong Python skills and experience building production-grade data workflows
Strong SQL skills with experience designing data models and transforming large datasets
Experience building and maintaining ETL or ELT pipelines and working with data warehouses or analytics databases
Familiarity with orchestration tools and workflow scheduling (for example Airflow, Dagster, Prefect, or similar)
Strong understanding of data quality, testing, observability, and operational best practices
Comfort working with large-scale datasets and troubleshooting performance issues
Ability to communicate clearly with technical and non-technical stakeholders

Nice To Haves

Experience working with identity, fraud, risk, compliance, or other regulated datasets
Experience integrating with external data sources, APIs, and government or registry data
Familiarity with streaming or near-real-time data patterns
Highly feedback-oriented with a desire for continuous improvement

Responsibilities

Design, build, and maintain scalable data pipelines that ingest, clean, validate, and transform data from internal systems and external sources
Own data reliability and quality through monitoring, alerting, lineage, and validation frameworks
Build and maintain data models and curated datasets that support analytics, dashboards, customer reporting, and downstream ML use cases
Partner with Engineering to define best practices for data architecture, storage, access controls, and performance
Implement orchestration and scheduling for batch and near-real-time workflows as needed
Optimize pipeline performance, cost, and scalability as data volumes grow
Develop and maintain documentation and runbooks for pipelines, datasets, and operational procedures
Identify data gaps and instrumentation needs, and work with engineering teams to improve event capture and logging