Staff Engineer – Data Platform

Jobtailor; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor is seeking a Senior Data Platform Engineer to lead the design and delivery of a scalable data ingestion and normalization platform. You will architect pipelines across ad network APIs and data warehouses, partner with DS/ML teams, and mentor senior engineers while improving observability and reliability.

You will drive architectural decisions in the GCP/BigQuery/dbt stack, balance cost and performance, and champion data contracts and SLAs with stakeholders across engineering and

Qualifications

• 10+ years of software engineering experience with 4+ years building production data platforms at scale.
• Staff-level technical leadership with direction across multiple workstreams and mentoring.
• Deep expertise in Python and SQL/dbt, with fluency in a modern orchestrator and data warehouse.
• Own non-trivial data platform: schema design, evolution, data quality, lineage, cost, and reliability.
• Strong product judgment, translating DS/ML needs into clean data contracts.
• Excellent written and verbal communication; defend technical decisions to stakeholders.

Responsibilities

• Be the tech lead and architect for data ingestion/normalization pipelines across multiple adapters.
• Design and lead backfills, lineage, time-travel, and observability for data platforms.
• Drive architecture in GCP/BigQuery/dbt and document designs for cross-team alignment.
• Mentor senior engineers and raise the engineering bar via reviews.
• Partner with data science to translate needs into pipeline contracts and SLAs.
• Own incident response and post-mortems; push systemic fixes.
• Drive AI workflows for data quality and analytics.
• Influence the data strategy across the engineering org.

Skills

Python

SQL

dbt

Dagster

Airflow

Temporal

BigQuery

Snowflake

Data platform design

Staff-level leadership

Mentoring engineers

Technical writing

Communication

Tools

Dagster

Airflow

Temporal

BigQuery

Snowflake

Job description

Responsibilities

• Be the tech‑lead and architect for Haus's data ingestion and normalization platform — ad network APIs (Google, Meta, TikTok, Amazon, etc.), Fivetran connectors, and customer warehouses (Snowflake, BigQuery) — balancing throughput, cost, and reliability.
• Design and lead implementation of high‑leverage systems: schema evolution, data contracts, DQ frameworks, idempotent backfills, lineage, time‑travel, data reproducibility and pipeline observability.
• Drive architectural decisions in our GCP / BigQuery / dbt stack — build vs. buy, what to standardize, what to deprecate — and write the design docs that align Engineering, DS, and Product teams.
• Raise the engineering bar through code review, design review, and mentorship; level up Senior engineers and unblock the team on the hardest problems.
• Partner with data science to translate fuzzy modeling and research needs into pipeline contracts and SLAs that downstream teams can trust.
• Own incident response and post‑mortems for critical pipeline failures; turn one‑off fires into systemic fixes.
• Drive design and implementation of AI (Agentic) workflows for data quality and analytics.
• Influence the broader engineering org's data strategy.

Requirements

• 10+ years of software engineering experience, with at least 4 years building production data platforms at meaningful scale (terabytes/day, hundreds of pipelines, or comparable).
• Track record of Staff‑level technical leadership: setting direction across multiple workstreams, writing design docs others build from, and mentoring senior engineers.
• Deep expertise in Python and SQL/dbt, with strong fluency in a modern orchestrator (Dagster, Airflow, Temporal, etc.) and a cloud data warehouse (BigQuery, Snowflake, etc.).
• Demonstrated ownership of a non‑trivial data platform — schema design, schema evolution, data quality, lineage, cost, and reliability — not just writing pipelines, but designing the system the pipelines live in.
• Strong product judgment — comfortable working with DS, ML, or analytics consumers and translating their needs into clean data contracts.
• Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders.