Engineering Manager, Data Platform

Chime Chime · Fintech · San Francisco, CA · Data Engineering

Engineering Manager for Data Platform, leading the Data Storage team. Responsible for building and operating scalable and secure storage foundations for analytics, experimentation, and AI-driven product experiences. Partners with other teams to define storage strategies, data contracts, and SLAs. Focuses on setting vision, fostering technical excellence, and growing a high-performing team.

What you'd actually do

  1. Own the strategy and roadmap for Chime’s Data Storage Platform, including Snowflake, data lake and online data stores for low-latency access.
  2. Design and evolve scalable, high-performance storage architecture that balance reliability, cost, and ease of use for both analytical and in-product workloads.
  3. Ensure performant and secure data access by defining and enforcing access patterns, partitioning and clustering strategies, indexing, and caching and serving layers for key datasets and metrics.
  4. Collaborate across Data Platform and partner teams to define clear data contracts, schemas, and SLAs between producers, storage, and consumers.
  5. Build tooling and automation for governance and compliance across sinks (e.g., RBAC, PII protection, tokenization, lineage, and auditability) in partnership with Security, Risk, and Compliance.

Skills

Required

  • Python
  • SQL
  • AWS
  • GCP
  • Azure
  • Snowflake
  • Spark
  • Flink
  • Kafka
  • Airflow
  • Kubernetes
  • Iceberg
  • RBAC
  • PII protection
  • tokenization
  • lineage
  • auditability

Nice to have

  • Java
  • Scala
  • performance tuning for analytical workloads
  • data governance
  • security
  • compliance

What the JD emphasized

  • 8+ years of experience in high-scale, high-reliability software development, with a focus on platforms, infrastructure, and data storage systems.
  • 3+ years of experience managing engineering teams, including hiring, performance management, and developing engineers.
  • track record of scaling products, platforms, and operations to support rapid growth in data volume, complexity, and criticality.
  • deep experience with data infrastructure components, such as data lakes and lakehouses (e.g., Iceberg), data warehouses (e.g., Snowflake), online and offline data stores, and both batch and real-time streaming systems.
  • proven expertise in system and data architecture for scalable, secure, and cost-efficient data platforms, including schema design, data modeling, and partitioning strategies.
  • Understand data governance, security, and compliance best practices (e.g., RBAC, PII handling, auditability) and have helped design systems that meet regulatory and internal standards.
  • Deeply interested in the transformative potential of advanced AI systems and how to build AI-ready data foundations (metadata, lineage, semantic layers, feature and metric serving).