Staff Software Engineer, Data Quality and Governance

Stripe
Seattle, WA
On-site

Who this role is best for

Strong fit for mid-level engineers with deep data governance experience who lead technical outcomes and collaborate across teams.

Best fit for

  • Mid-level engineers with 10+ years of data system experience and leadership potential
    — “Staff-level role — that typically means 10+ years of experience building and operating data systems
  • Candidates with strong distributed systems knowledge and a focus on data quality
    — “Strong distributed systems fundamentals are a must — this team's core services are high-availability infrastructure
  • Individuals who excel at resolving complex data inconsistencies and driving data quality initiatives
    — “An inquisitive nature in diving into data inconsistencies to pinpoint issues

Things to consider

  • The role demands high autonomy and responsibility in ambiguous environments
    — “Thrive with high autonomy and responsibility in an ambiguous environment
  • Candidates must be prepared to manage SLAs of data pipelines and full stack applications
    — “Develop strong subject matter expertise and manage the SLAs of data pipelines and full stack web applications

How to stand out

  • Highlight experience with Iceberg, Kafka, Flink, and Spark in your resume and interviews
    — “Our stack is made up of Iceberg, Kafka, Change Data Capture, Flink, Spark, Airflow, Hive Metastore, Pinot, Trino, and AWS Cloud
  • Demonstrate past success in creating data marts or warehouses for business reporting
    — “Experience creating and maintaining data marts / warehouses to power business reporting needs
  • Showcase leadership in mentoring engineers and delivering high-quality data solutions
    — “Lead the technical outcomes for a team of ambitious, talented engineers
  • Emphasize your ability to work with product managers and cross-functional teams on data initiatives
    — “Collaborate with product managers and peers across the company to create/improve canonical datasets
  • Demonstrate your ability to use AI/LLM tools for data analysis and problem-solving
    — “Leverage AI/LLM and Agents at scale to produce and analyze high-quality data on ambiguous problems
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid Level

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • technical outcomes
  • mentorship
  • guidance
  • support
  • canonical datasets
Typical background
data engineeringdata governance

Skills & requirements

Required

Data DiscoveryMetadataCatalog PlatformData PipelinesData WarehousesData GovernanceData QualityData LineageData ScoringAi/llmAgentsData InconsistenciesData Quality IssuesBackend DevelopmentDistributed SystemsCustomer FocusCross-functional Collaboration

Preferred

Open Source CommunitiesStandards BodiesProduction Enterprise Scale EnvironmentNew TechnologiesAdoption Or Operation

Stack & domain

Distributed SystemsData DiscoveryMetadataCatalog PlatformData PipelinesData WarehousesAi/llmAgentsData QualityData GovernanceData CatalogKnowledge Graph ServiceDataset TieringGovernance StandardsLineage TrackingQuality Scoring SystemBackend DevelopmentScalaJavaGoSQLMentorshipCollaborationProblem-solvingCommunicationTeam ManagementStrategic PlanningInnovationKnowledge SharingContinuous ImprovementData Engineering

About the role

Original posting from Stripe

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Data Quality and Governance owns the infrastructure that makes Stripe's data trustworthy and findable - the Data Catalog, the Knowledge Graph Service, dataset tiering and governance standards, lineage tracking, and the quality scoring system (DQPD) that every engineering team reports against. They're the team that defines what "good data" means at Stripe and then builds the enforcement and measurement tools to drive adoption across the company.

What you’ll do

Responsibilities

Lead the technical outcomes for a team of ambitious, talented engineers, providing mentorship, guidance, and support to ensure their success

Build and operate large-scale data discovery, metadata, or catalog platform

Develop strong subject matter expertise and manage the SLAs of data pipelines and full stack web applications that support critical stakeholders

Collaborate with product managers and peers across the company to create/improve canonical datasets and data warehouses, use golden paths, and ensure Stripes and customers are using trustworthy data

Leverage AI/LLM and Agents at scale to produce and analyze high-quality data on ambiguous problems

Have the opportunity to drive the execution of key data initiatives for Stripe, overseeing the entire development lifecycle from planning to delivery while maintaining high standards of quality and timely completion

Foster a collaborative and inclusive work environment, promoting innovation, knowledge sharing, and continuous improvement within the team

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

This is a Staff-level role — that typically means 10+ years of experience building and operating data systems, pipelines, warehouses, infrastructure, and leading teams to deliver exceptional solutions

Strong distributed systems fundamentals are a must — this team's core services are high-availability infrastructure that the rest of Stripe's data tooling depends on

An inquisitive nature in diving into data inconsistencies to pinpoint issues, and resolve deep rooted data quality issues

Knowledge of a backend development language (such as Scala, Java, or Go) and strong SQL experience

Extreme customer focus, with a commitment to partnering with product, leaders across the business, and other Stripe engineers to understand their use cases

Effective cross-functional collaboration, with the ability to think rigorously, communicate clearly, and make or coordinate difficult decisions and trade-offs

Thrive with high autonomy and responsibility in an ambiguous environment

Ability to foster and work in a healthy, inclusive, challenging, and supportive work environment

Preferred qualifications

Our stack is made up of Iceberg, Kafka, Change Data Capture, Flink, Spark, Airflow, Hive Metastore, Pinot, Trino, and AWS Cloud - experience with all or some of these tools is a huge plus

Influencing open-source contributions

Experience creating and maintaining data marts / warehouses to power business reporting needs

Experience collaborating with Product, Go-To-Market, or Sales / Marketing teams

Genuine enjoyment of innovation and a deep interest in understanding how things work, with the ability to question and direct architectural decisions

Strong written and verbal communication skills for various audiences, including leadership, users, and company-wide

Source: Stripe careers

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