Home Based Data Storage Jobs in California (NOW HIRING)

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.

This role is not just about running infrastructure - it's about setting a vision for the future of data storage at Chime, fostering a culture of technical excellence, and growing a high-performing team that can evolve our architecture as the business and our data scale.

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.

• 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.

• 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.

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