Staff Data Quality & Governance Engineer

Stripe

Chicago (IL)

Hybrid

USD 204,000 - 305,000

Full time

2 days ago
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Job summary

Stripe is seeking a Staff Data Platform Engineer to lead the Data Quality and Governance team in Chicago. You will drive the design of large-scale data catalogs, metadata systems, and data warehouses, partnering with product teams to ensure trustworthy data across Stripe.

You’ll mentor engineers, own critical SLAs for pipelines and full-stack data applications, and apply AI/LLM solutions at scale to complex data problems while promoting an inclusive, high-autonomy culture.

Qualifications

  • 10+ years of experience building and operating data systems, pipelines, warehouses, and leading teams
  • Strong distributed systems fundamentals are required for high-availability data tooling
  • Ability to dive into data inconsistencies and resolve quality issues

Responsibilities

  • Lead the technical outcomes for a team of engineers and provide mentorship
  • Build and operate large-scale data discovery and catalog platforms
  • Develop subject matter expertise and manage SLAs of data pipelines and full-stack data applications
  • Collaborate with product managers to create canonical datasets and data warehouses
  • Leverage AI/LLM and agents to analyze high-quality data on ambiguous problems
  • Drive the execution of key data initiatives end-to-end from planning to delivery
  • Foster a collaborative, inclusive, and high-autonomy team culture

Skills

Scala/Java/Go
SQL
Distributed systems
Data pipelines

Tools

Iceberg
Kafka
Flink
Spark
Airflow
Hive Metastore
Pinot
Trino
AWS

Job description

Stripe is seeking a Staff Data Platform Engineer to lead the Data Quality and Governance team in Chicago. You will drive the design of large-scale data catalogs, metadata systems, and data warehouses, partnering with product teams to ensure trustworthy data across Stripe.

You’ll mentor engineers, own critical SLAs for pipelines and full-stack data applications, and apply AI/LLM solutions at scale to complex data problems while promoting an inclusive, high-autonomy culture.

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