Stripe
About this role
Stripe is the payments infrastructure millions of businesses run on, and this role leads the Data Engineering Solutions team through high impact data problems at that scale. The posting is specific about what it wants: someone who has built data pipelines for large volume and is deeply knowledgeable across the standard toolchain, naming Airflow, Spark, Kafka and Flink. Alongside the technical depth it asks for something less common in staff engineering postings, describing the ideal candidate as empathetic and excellent at building strong relationships, because the team works across Stripe to understand other teams' use cases and unlock new capabilities. So this is a staff seat where the influence half of the job is stated as plainly as the engineering half. The requirement is 10 or more years of engineering experience with 5 or more hands on building and operating data systems, pipelines, datasets, warehouses and infrastructure.
Who this is for
What the posting requires as minimum:
- 10+ years of engineering experience, with 5+ years of hands on experience building and operating data systems and pipelines, datasets and data warehouses, infrastructure, and leading small teams to deliver solutions.
- A strong engineering background and a genuine interest in data.
- Prior experience writing and debugging data pipelines using a distributed data framework such as Spark, Hadoop or Trino.
- An inquisitive nature, diving into data inconsistencies to pinpoint issues and resolve deep rooted data problems.
Stripe states that the preferred qualifications are a bonus rather than a requirement, and that if you meet the minimum requirements you are encouraged to apply.
What the work actually looks like:
- Lead the technical outcomes for a team of engineers working on high impact, cutting edge data problems.
- Build and operate data pipelines at large scale using the modern data engineering toolchain: Airflow, Spark, Kafka and Flink are named in the posting.
- Collaborate with other Stripe teams to understand their use cases and unlock new capabilities for them.
- Build strong working relationships across the company, which the posting treats as a core requirement rather than a soft extra.
What the emphasis on empathy tells you: for a staff role at this scale, most of the hard problems are cross team. The posting naming empathy and relationship building alongside Spark and Kafka is a signal about how the role is actually evaluated.
Location and working pattern: Bengaluru, India. Office days are not stated in the posting.
Honest fit guidance: ten years is a real bar and the 5 years of hands on data systems work is the substantive part. The phrase leading small teams appears in the minimum requirements, so this is a technical leadership role even though it is an individual contributor title. If your data engineering has been solo or within one team, the cross organisation scope is the stretch. If you have led data platform work that other teams depended on, that is exactly the story to tell.
- 10+ years of engineering experience, with 5+ years of hands on experience building and operating data systems and pipelines, datasets and data warehouses, infrastructure, and leading small teams to deliver solutions.
- A strong engineering background and a genuine interest in data.
- Prior experience writing and debugging data pipelines using a distributed data framework such as Spark, Hadoop or Trino.
- An inquisitive nature, diving into data inconsistencies to pinpoint issues and resolve deep rooted data problems.
Stripe states that the preferred qualifications are a bonus rather than a requirement, and that if you meet the minimum requirements you are encouraged to apply.
What the work actually looks like:
- Lead the technical outcomes for a team of engineers working on high impact, cutting edge data problems.
- Build and operate data pipelines at large scale using the modern data engineering toolchain: Airflow, Spark, Kafka and Flink are named in the posting.
- Collaborate with other Stripe teams to understand their use cases and unlock new capabilities for them.
- Build strong working relationships across the company, which the posting treats as a core requirement rather than a soft extra.
What the emphasis on empathy tells you: for a staff role at this scale, most of the hard problems are cross team. The posting naming empathy and relationship building alongside Spark and Kafka is a signal about how the role is actually evaluated.
Location and working pattern: Bengaluru, India. Office days are not stated in the posting.
Honest fit guidance: ten years is a real bar and the 5 years of hands on data systems work is the substantive part. The phrase leading small teams appears in the minimum requirements, so this is a technical leadership role even though it is an individual contributor title. If your data engineering has been solo or within one team, the cross organisation scope is the stretch. If you have led data platform work that other teams depended on, that is exactly the story to tell.
Apply on company site
Opens stripe.com, the employer's own application page. Applying is always free.