Senior Data Engineer

Tatari•Los Angeles, CA

1h•$170,000 - $200,000•Hybrid

About The Position

As a Senior Data Engineer in our Reporting and Measure pillar, you will design, build and own the large-scale data pipelines that turn high-volume raw data into curated, production-grade datasets powering Tatari’s reporting and measurement products. You will be responsible for taking data products from requirements through tech spec, implementation, validation and production rollout—partnering closely with data science on methodology, backend and frontend engineers on serving layers, and product on requirements. The ideal candidate will have deep expertise in building and operating ETL pipelines at scale, a strong data quality and validation mindset, and a proven track record of owning datasets end-to-end in a cross-functional environment.

Requirements

5+ years of dedicated experience designing, building, and operating production ETL pipelines at scale.
Strong programming skills in Python (or a similar language) and SQL.
Deep hands-on experience with Spark/PySpark.
Experience with workflow orchestration tools such as Airflow, including DAG design, backfills, and failure recovery.
Strong data modeling skills for analytics (curated/gold-layer datasets, schema design, partitioning, and data contracts consumed by downstream services).
Ongoing interest in agentic developer workflows (testing agent harnesses, building them, agent loop engineering, and similar emerging paradigms).
A rigorous approach to data quality (validation, reconciliation, and methodology comparison as part of shipping any dataset).
Experience debugging distributed data systems (performance tuning, memory issues, and optimizations).
Strong written communication and cross-functional collaboration skills.

Nice To Haves

Experience with OLAP stores (e.g., ClickHouse).

Responsibilities

Design, build, and own large-scale data pipelines.
Transform high-volume raw data into curated, production-grade datasets.
Power Tatari’s reporting and measurement products.
Take data products from requirements through tech spec, implementation, validation, and production rollout.
Partner with data science on methodology.
Collaborate with backend and frontend engineers on serving layers.
Work with product on requirements.
Ensure data quality and validation.
Own datasets end-to-end in a cross-functional environment.
Debug distributed data systems, including performance tuning, memory issues, and optimizations.
Write technical specifications and incident reports.