Senior Data Engineering Manager

Want to help everyday Americans invest and build wealth? Financial inequality is increasing, and too many people are getting left behind. At Stash, we’re passionate about democratizing wealth creation through education, advice, and products that help customers achieve greater financial freedom.

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.

This position operates on a hybrid schedule, requiring you to be on-site in our New York office at least three days per week to work with stakeholders and foster team collaboration.

What you'll do:

• 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.

What we're looking for:

• 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.

Gold Stars:

• Experience working in fintech, financial services, brokerage, banking, or another regulated environment.
• Experience supporting production data science or machine-learning workflows.
• Working knowledge of dbt and modern analytics engineering practices.
• Experience evolving a platform from manual operations toward reliable self-service and automation.
• Experience with Datadog or similar observability platforms for pipelines and infrastructure.
• Experience evaluating or implementing higher-frequency or real-time data patterns when the use case justified them.
• Experience using AI coding assistants or agentic workflows in production engineering work, with appropriate validation and security judgment.