Senior Software Engineer, Data Platform

About the Role

Chime’s Data Platform team builds the infrastructure every engineering and analytics team depends on - ingestion, transformation, quality, governance, and self-serve tooling across batch and streaming workloads.

You’ll own core platform systems at scale, design the frameworks other teams build on, and set the technical standards for how data moves through Chime. High autonomy, real architectural trade-offs, production impact from day one. We’re hiring multiple engineers into this role at the senior level.

In this role, you can expect to

• Design, build, and operate self-serve ETL/ELT frameworks supporting both batch and streaming workloads, with direct ownership of pipeline reliability, data quality SLAs, and schema evolution
• Drive the technical roadmap for our data platform - evaluate build-vs-buy decisions, define integration patterns, and set standards that engineering teams across the company adopt
• Partner with Product Engineering, Data Science, Analytics, and Marketing as a technical lead to design data contracts, onboard new data domains, and scale ingestion without sacrificing governance
• Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling)
• Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products
• Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers
• Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

To thrive in this role, you have

• 5+ years building, shipping, and operating data infrastructure in production - not just writing pipelines, but owning their reliability, performance, and cost at scale
• Strong system design skills - you’ve written design docs, made trade-offs, and delivered the result
• Solid understanding of key metrics for data pipelines and experience building solutions to provide visibility to partner teams
• Deep proficiency in Python or Java/Kotlin, with strong opinions on testing, code quality, and maintainability in data-heavy codebases
• Production experience with the modern data stack, specifically:
• A cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Workflow orchestration (Airflow, Dagster, or Prefect) and IaC (Terraform)
• At least one streaming technology (Kafka, Flink, or Kinesis) - you understand when batch isn’t enough
• Data modeling and transformation frameworks (dbt)
• Built or improved observability for data systems - freshness monitoring, row-level qu