5+ years of experience in data engineering, focusing on large-scale data lake or warehouse environments.
5+ years of experience working with SQL and distributed query engines (e.g., Spark, BigQuery, Snowflake).
Deep proficiency with pipeline orchestration tools (e.g., Airflow, Prefect) and transformation frameworks (e.g., Spark).
Experience designing and implementing data quality frameworks including validation and anomaly detection.
Familiarity with observability tooling for monitoring, alerting, and incident response in data systems.
Experience enabling non-engineering stakeholders to self-serve on data infrastructure.
Responsibilities
Design, build, and maintain scalable data pipelines and ingestion workflows for a growing Data Lake.
Define and enforce data quality standards and validation frameworks.
Continuously optimize existing pipelines for performance and cost efficiency.
Expand and own monitoring and alerting coverage to detect data issues proactively.
Drive best practices in data modeling, partitioning, and resource utilization.
Engage in hands-on tasks, including writing and debugging pipelines.
Assist internal teams in building data infrastructure effectively.
Benefits
Flexible roles accommodating different skill sets and locations.
Opportunities for hands-on work with advanced data tools and technologies.
Full Job Description
The Role
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.
What you’ll do
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.
What we’re looking for
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
Preferred Qualifications
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.
