Senior Software Engineer, Data Platform

About This RoleAI processing…

Key Responsibilities

• 1
  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
• 2
  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
• 3
  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
• 4
  Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling)
• 5
  Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products
• 6
  Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers
• 7
  Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

Requirements

• 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 quality checks, schema drift detection, and SLA dashboards that engineering and business teams actually trust
• Experience operating in a regulated environment (fintech, healthtech, or similar) where data governance, access control, and audit trails are non-negotiable

Perks & Benefits