Data Engineer

At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage.

This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us.

Our Data Platform is growing fast - more users, more data sources, more pipelines - and the expectations that come with that growth are rising accordingly. We need a Senior Data Engineer who can work across the stack: tighten up our ingestion pipelines, write new ETL pipelines wherever needed, build out monitoring and alerting where we have blind spots, and help internal teams build their own data infrastructure “the right way”. You'll spend real time in the weeds - writing and debugging pipelines, optimizing queries, reviewing what others have built - and you should be comfortable with that.

This is a high-impact role. Our data is increasingly tied to the experiences we deliver to customers, which means data quality, accuracy, and observability are no longer just engineering concerns - they directly affect trust. You'll be at the center of that challenge.

• You will design, build, and maintain robust, scalable data pipelines and ingestion workflows across a growing Data Lake;

• Define and enforce data quality standards, SLOs, and validation frameworks to ensure accuracy and reliability of critical data assets;

• Continuously optimize existing pipelines for performance and cost efficiency as data volumes scale;

• Expand and own our monitoring and alerting coverage — surfacing data issues before they become customer-facing problems;

• Drive best practices around data modeling, partitioning, and compute resource utilization;

• Also, you get to drive 100,000 lb excavators.

• 5+ years of experience in data engineering, with a strong track record in large-scale data lake or data warehouse environments

• 5+ years of experience working with SQL and distributed query engines (e.g. Spark, BigQuery, Snowflake, or similar)

• Deep proficiency with pipeline orchestration tools (e.g. Airflow, Prefect, or equivalent) and transformation frameworks (e.g. Spark)

• Experience designing and implementing data quality frameworks - validation, anomaly detection, lineage tracking

• Familiarity with observability tooling for data systems: monitoring, alerting, and incident response for data pipelines

• Experience enabling non-engineering stakeholders to self-serve on data infrastructure, whether through documentation, tooling, or hands-on enablement

• Hands-on experience with Databricks and Spark

• Experience with streaming or near-real-time ingestion patterns

• Familiarity with data governance and access control at scale

• Background working on customer-facing data products or external SLAs

Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.