Engineering Manager, Data Platform

About This RoleAI processing…

Key Responsibilities

• 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.
• 6
  Manage and grow a team of engineers, setting clear expectations, providing coaching and feedback, and raising the bar on engineering quality and operational excellence.
• 7
  Establish strong operational practices, including on-call, incident management, postmortems, and SLOs for the storage and serving layers your team owns.
• 8
  Stay ahead of industry trends in data storage, lakehouse architectures, and AI/ML-ready data systems, and thoughtfully introduce technologies that improve our platform’s capabilities.

Requirements

• Have 8+ years of experience in high-scale, high-reliability software development, with a focus on platforms, infrastructure, and data storage systems.
• Have 3+ years of experience managing engineering teams, including hiring, performance management, and developing engineers.
• Have a track record of scaling products, platforms, and operations to support rapid growth in data volume, complexity, and criticality.
• Bring 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.
• Have proven expertise in system and data architecture for scalable, secure, and cost-efficient data platforms, including schema design, data modeling, and partitioning strategies.
• Are comfortable working with modern data and infrastructure technologies, such as Spark, Flink, Kafka, Airflow, Kubernetes, and similar tools.
• Are proficient in Python or similar languages (e.g., Java, Scala) and familiar with SQL and performance tuning for analytical workloads.
• Have extensive experience in cloud-based data ecosystems, such as AWS (S3, DynamoDB, Redshift, Snowflake, EMR), GCP (BigQuery, Dataflow), or Azure equivalents.
• 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.
• Are 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).
• Excel at building strong relationships with stakeholders across engineering, product, analytics, security, and finance, and can translate between technical and business contexts.
• Demonstrate strong people leadership, with a track record of building a culture of belonging and engineering excellence.

Perks & Benefits