Engineering Manager, Data Platform

About The Role

As an Engineering Manager on Chime’s Data Platform, leading the Data Storage team, you will own the group that manages Chime’s online and analytical data stores and low-latency metric serving layer. Your team will be responsible for building and operating reliable, scalable, and secure storage foundations that power analytics, experimentation, and AI-driven product experiences across Chime. You’ll partner closely with other Data Platform squads as well as Product Engineering, Security, and Analytics teams to define storage strategies, data contracts, and SLAs that support both high-scale batch workloads and latency-sensitive online use cases. Your leadership will directly impact how Chime stores, governs, and serves data - enabling trusted, self-serve, AI-ready data for Chimers across the company.

The base salary offered for this role and level of experience will begin at $199,000 and up to $275,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

Responsibilities

• Own the strategy and roadmap for Chime’s Data Storage Platform, including Snowflake, data lake and online data stores for low-latency access.
• Design and evolve scalable, high-performance storage architecture that balance reliability, cost, and ease of use for both analytical and in-product workloads.
• 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.
• Collaborate across Data Platform and partner teams to define clear data contracts, schemas, and SLAs between producers, storage, and consumers.
• 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.
• Manage and grow a team of engineers, setting clear expectations, providing coaching and feedback, and raising the bar on engineering quality and operational excellence.
• Establish strong operational practices, including on-call, incident management, postmortems, and SLOs for the storage and serving layers your team owns.
• 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

• 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.
• A 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.
• Comfortable working with modern data and infrastructure technologies, such as Spark, Flink, Kafka, Airflow, Kubernetes, and similar tools.
• Proficiency in Python or similar languages (e.g., Java, Scala) and familiarity with SQL and performance tuning for analytical workloads.
• Extensive experience in cloud-based data ecosystems, such as AWS (S3, DynamoDB, Redshift, Snowflake, EMR), GCP (BigQuery, Dataflow), or Azure equivalents.
• Understanding of data governance, security, and compliance best practices (e.g., RBAC, PII handling, auditability) and experience designing systems that meet regulatory and internal standards.
• Deep interest in the transformative potential of advanced AI systems and how to build AI-ready data foundations (metadata, lineage, semantic layers, feature and metric serving).
• Ability to build strong relationships with stakeholders across engineering, product, analytics, security, and finance, and translate between technical and business contexts.
• Strong people leadership, with a track record of building a culture of belonging and engineering excellence.

Qualifications

• BS/MS in Computer Science, Engineering, or a related field.
• Experience with distributed systems, databases, and data warehousing.
• Knowledge of cloud-native technologies and services.
• Experience with data modeling, data warehousing, and data governance.
• Experience with data privacy and security best practices.
• Experience with data analytics and machine learning.

Skills

• Leadership and strategic thinking.
• Technical expertise in data storage and management.
• Collaboration and communication skills.
• Problem-solving and decision-making abilities.
• Project management and organizational skills.
• Cloud-native technologies and services.
• Data modeling and data warehousing.
• Data privacy and security best practices.
• Data analytics and machine learning.

Benefits

In this role, you can expect to Own the strategy and roadmap for Chime’s Data Storage Platform, including Snowflake, data lake and online data stores for low-latency access.Design and evolve scalable, high-performance storage architecture that balance reliability, cost, and ease of use for both analytical and in-product workloads.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.Collaborate across Data Platform and partner teams to define clear data contracts, schemas, and SLAs between producers, storage, and consumers.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.Manage and grow a team of engineers, setting clear expectations, providing coaching and feedback, and raising the bar on engineering quality and operational excellence.Establish strong operational practices, including on-call, incident management, postmortems, and SLOs for the storage and serving layers your team owns.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.

Pay

The base salary offered for this role and level of experience will begin at $199,000 and up to $275,000. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.

Schedule

In this role, you can expect to Own the strategy and roadmap for Chime’s Data Storage Platform, including Snowflake, data lake and online data stores for low-latency access.Design and evolve scalable, high-performance storage architecture that balance reliability, cost, and ease of use for both analytical and in-product workloads.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.Collaborate across Data Platform and partner teams to define clear data contracts, schemas, and SLAs between producers, storage, and consumers.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.Manage and grow a team of engineers, setting clear expectations, providing coaching and feedback, and raising the bar on engineering quality and operational excellence.Establish strong operational practices, including on-call, incident management, postmortems, and SLOs for the storage and serving layers your team owns.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.