Senior Data Engineering Manager, Stash

• 8+ years of experience in data engineering, platform engineering, or a related field, including experience leading or managing data engineers
• Experience leading a small team where you remained close enough to the work to review designs, guide incidents, and unblock difficult technical decisions
• Strong production experience building or supporting distributed data pipelines using tools such as EMR, Glue, Delta, Iceberg, Databricks and orchestration tools
• Hands-on experience designing, deploying and operating data workloads in AWS cloud environment
• Strong programming(preferred python and scala) and SQL experience in your background
• Experience designing, implementing, and operating real-time or streaming data pipelines
• A track record of operating business-critical data systems, including observability, incident response, data quality, performance, and cost management
• Experience introducing or improving CI/CD, Infrastructure as Code, automated testing, code review, and secure deployment practices
• Strong architectural judgment. You can distinguish a real scaling requirement from a process or reliability problem, and you prefer maintainable systems over unnecessary complexity
• Experience defining ownership across Data Engineering, Backend Engineering, Infrastructure or SRE, Analytics Engineering, and downstream data consumers
• Strong people leadership. You can develop experienced engineers, delegate meaningful ownership, give direct feedback, and hire thoughtfully as the team grows
• Clear communication with both technical and non-technical partners. You can explain an incident, an architecture decision, or a delayed commitment without hiding the tradeoff in jargon
• Sound judgment in a regulated environment, including secure data handling, least-privilege access, audit-ability, and change control

Desirable

• We’re looking for a Senior Manager, Data Engineering to lead and grow the team responsible for the data platform behind Stash’s analytics, data science, operations, and business reporting
• This is a hands-on leadership role
• You’ll set the technical direction for our AWS-based data platform, develop the engineers on your team, and strengthen how Data Engineering works with Backend Engineering, Infrastructure, SRE, Analytics Engineering, and Data Science
• Your first mandate is practical: improve reliability, reduce manual operational work, modernize how we deploy and monitor pipelines, and create clear ownership for how new data enters the platform
• You’ll inherit meaningful systems and real constraints—not a blank slate. The right person will know when to modernize, when to simplify, and when the best architecture is the one the team can operate reliably
• Lead and grow the Data Engineering team. Coach engineers, set clear expectations, create development opportunities, and build a culture of ownership, accountability, and technical excellence
• Own the Data Engineering roadmap. Translate company priorities and platform risks into a sequenced plan that balances reliability, automation, security, cost, and new capabilities
• Raise the operational bar. Improve observability, alerting, runbooks, incident response, service-level expectations, and recovery practices so problems are found and resolved before downstream teams lose trust
• Reduce manual work. Identify repetitive and fragile operational processes, prioritize them by risk and effort, and automate the work that consumes engineering capacity
• Modernize how the platform ships. Establish CI/CD, Infrastructure as Code, testing, code review, documentation, and rollback practices for data pipelines and supporting infrastructure
• Evolve our AWS data platform. Guide architecture and hands-on design across ingestion, distributed processing, storage, and warehouse delivery. Improve performance and cost without introducing unnecessary complexity
• Establish clear ingestion ownership with Engineering. Partner with Backend, Infrastructure, SRE, and Security so new product systems ship with reliable data contracts, production-ready ingestion, appropriate access controls, and named operational owners
• Protect data quality from source to warehouse. Define standards for schemas, keys, grain, semantics, validation, lineage, backfills, and cutovers. Ensure source changes do not silently alter downstream metrics or models
• Partner with Analytics Engineering and Data Science. Deliver reliable, well-documented platform inputs and agree on clear boundaries between ingestion, source normalization, business-facing marts, and analytical use cases
• Make sound architecture tradeoffs. Evaluate batch, higher-frequency batch, and real-time patterns based on the business requirement, operational cost, and team capacity—not novelty
• Communicate clearly. Give technical and business leaders a direct view of platform health, risks, priorities, tradeoffs, and progress
• Use AI tools with judgment. Apply AI-assisted development and operational workflows where they improve quality and speed, while protecting sensitive data and owning the correctness of the output