About the Role

The Data Governance function is pivotal in ensuring the integrity, trustworthiness, and effective management of Chime’s data assets. Our mission is to establish and operationalize data governance frameworks that not only meet compliance requirements, but actively enable high-confidence decision making across the company.  As a Data Governance Engineer, you will develop and implement policies and tools for data quality, developer enablement, certified datasets, and governance automation - with a sharp focus on building trust signals and scorecards that help data consumers quickly understand and act on the reliability of Chime's data.

This is a hands-on engineering role. You will write production-quality code to build automation for governance and data quality processes using Terraform, Python (or similar languages), and contribute to internal libraries, frameworks, and orchestration workflows that enable scalable governance.

You will partner closely with data engineering, analytics, product engineering, and compliance teams to:

• Support Chime in data compliance and risk reduction initiatives
• Establish data quality as a product discipline
• Build trust signals and scorecards that make data reliability transparent and data context valuable for a variety of use cases
• Implement governance workflows upstream, where data are created
• Ensure data context and metadata are sufficient for downstream use cases, including AI applications

You will be involved in reviews of technical designs and implementation plans to provide guidance on appropriate development from a data governance and compliance perspective, acting as a trusted advisor to teams innovating at Chime. You will also be a strong advocate and leader for Artificial Intelligence tooling and adoption at Chime, bringing curiosity and a practical eye for where AI creates leverage in data governance workflows. This role is 40% strategic influence, 40% development and building, and 20% stakeholder management and support.

The base salary for this role and level of experience will begin at $160,000.00 and go up to $221,000.00. 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.

In this role, you can expect to

• Build Data Trust Infrastructure: Design and implement trust scorecards, trust signals, and quality indicators that give data consumers real-time visibility into data reliability - and partner with producers upstream to embed quality checks at the source before issues propagate downstream.
• Drive Data Quality Strategy: Own and evolve Chime's data quality strategy end-to-end. Design and partner with engineering teams to implement data quality frameworks and monitoring systems that proactively identify, surface, and resolve data issues at scale.
• Develop and Implement Data Governance Policies: Create and enforce policies for data classification, quality, and lifecycle management, ensuring data integrity and compliance with SOX and other applicable regulatory standards.
• Enable the Data Catalog: Collaborate on the development, adoption, and enrichment of Chime's data catalog - improving discoverability, context richness (lineage, ownership, definitions, usage guidance), and the overall experience of working with Chime's data.
• Automate Governance Processes: Develop and deploy automation solutions for data governance tasks, such as metadata management, data lineage tracking, access controls, and quality remediation workflows.
• Champion SOX Compliance: Play an active role in Chime's SOX compliance efforts, ensuring that data governance practices, controls, and audit trails meet regulatory requirements and are well-documented.
• Lead AI Adoption for Data Governance: Serve as a leader and advocate for AI tooling within the data governance space - identifying opportunities to apply AI to automate governance workflows, improve data context, enhance trust scoring, and accelerate adoption of governance best practices across engineering teams.
• Collaborate Across Teams: Work closely with data engineers, analysts, and compliance officers to align data governance initiatives with business needs and regulatory requirements.

To thrive in this role, you have

• Experience: 5+ years in data engineering, data governance, or a related field, with hands-on experience in data classification, cataloging, quality assurance, and trust frameworks. Engineering ability to design and implement data governance and quality processes is critical to success.
• Trust & Quality Expertise: Demonstrated experience building data trust signals, quality scorecards, or similar frameworks that make data reliability visible and actionable for both engineers and non-technical stakeholders.
• Programming Experience: Proficiency in Python or a comparable programming language, and experience building scalable data tooling or automation pipelines.
• Curiosity and Relentlessness: You are genuinely curious about data, technology, and the problems that sit at the intersection of the two. You don't let ambiguity stop you - you dig in, ask the right questions, and keep pushing until you've found the right answer or built the right solution. You bring energy and persistence to hard problems and don't need external pressure to drive yourself forward.
• Composure in Complexity: Data governance at a fintech means navigating competing priorities, regulatory constraints, sprawling data ecosystems, and stakeholders with different mental models of "good." You thrive in this environment rather than shrinking from it - you can hold complexity without getting overwhelmed, break it into manageable pieces, and make steady progress without waiting for perfect conditions.
• Exceptional Communication: You are a terrific communicator in both verbal and written form. You can write a crisp one-pager that earns buy-in from a skeptical audience, run a meeting that ends with clarity instead of confusion, and translate deeply technical concepts into plain language that moves people to action. You treat communication as a core part of the job, not an afterthought.
• Product Management Mindset: You think like a product manager as much as an engineer. You ask "who is this for and what problem does it solve?" before writing a line of code. You prioritize ruthlessly, define success before you start building, and care about adoption and outcomes - not just delivery. You treat your internal stakeholders as customers and design solutions with their experience in mind.
• AI Curiosity and Advocacy: Genuine curiosity about AI and its applications in the data governance space, with an appetite for identifying and piloting new tools and workflows that improve the experience of working with Chime's data.
• Technical Proficiency: Strong knowledge of data governance tools and platforms, and experience with cloud data warehouses like Snowflake. Experience with data catalog platforms and driving adoption across engineering organizations is a strong plus.
• Bonus: Compliance Knowledge: Familiarity with SOX, CCPA, and other data privacy and financial regulations, and ability to apply technical skills to governance practices that meet audit and compliance requirements.

#LI-Onsite #LI-GC1

About the Role

The Data engineering team has been created keeping in mind the importance and criticality of data for the overall success of Chime.  The mission of this group is to build scalable data artifacts and insights that enable accurate, governed, data-driven decision-making.. As a core member of the team, you will have the opportunity to build scalable data pipelines and frameworks.  Additionally, you will have the unique opportunity to architect and build workflows that could potentially become de facto standards for the fintech industry.

The base salary offered for this role and level of experience will begin at $164,000.00 and go up to $227,000.00. 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.

In this role, you can expect to

• Be a hands-on data engineer, building, scaling and optimizing ETL pipelines
• Design data schemas and scale them data  for 10x data growth 
• Ownership of all aspects of data - data quality, data governance, schema design, data quality and security
• Develop scalable data architecture and standardized data models that enable self-serve data insights using AI.
• Own the ETL workflows and make sure the pipeline meets data quality and availability requirements
• Work closely with partner teams, like Data Science, Analytics and DevOps

To thrive in this role, you have

• 5+ years experience transforming data to governed and lucid datasets
• 5+ years of hands-on experience to build and deploy production-quality data pipelines
• 5+ years of experience working with stakeholders to provide business insights
• 5+ years of experience writing SQL, Spark, AWS Glue, EMR, Airflow, Python 
• 3+ years of hands-on experience using any MPP database system like Snowflake, AWS Redshift or Teradata
• Track record of successful partnerships with Analytics, Data Science and DevOps teams 
• Understanding of key metrics for data pipelines and has built solutions to provide visibility to partner teams
• Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

#LI-Onsite #LI-GC1

About the Role

Chime’s Data Platform team builds the infrastructure every engineering and analytics team depends on - ingestion, transformation, quality, governance, and self-serve tooling across batch and streaming workloads.

You’ll own core platform systems at scale, design the frameworks other teams build on, and set the technical standards for how data moves through Chime. High autonomy, real architectural trade-offs, production impact from day one. We’re hiring multiple engineers into this role at the senior level.

The base salary offered for this role and level of experience will begin at $187,000.00 and up to $259,000.00. 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.

In this role, you can expect to

• Design, build, and operate self-serve ETL/ELT frameworks supporting both batch and streaming workloads, with direct ownership of pipeline reliability, data quality SLAs, and schema evolution
• Drive the technical roadmap for our data platform - evaluate build-vs-buy decisions, define integration patterns, and set standards that engineering teams across the company adopt
• Partner with Product Engineering, Data Science, Analytics, and Marketing as a technical lead to design data contracts, onboard new data domains, and scale ingestion without sacrificing governance
• Architect and enforce data lineage, schema registry, and access controls across all domains - ensuring compliance with fintech regulatory requirements (SOX, PII handling)
• Own the platform - monitor, alert, debug, and resolve incidents across the data platform with the urgency expected of infrastructure that powers financial products
• Raise the technical bar of the team through code review, design reviews, and hands-on mentorship of junior and mid-level engineers
• Engineers are required to participate in on-call rotation. Being on call may include responding to incidents outside of regular working hours when necessary.

To thrive in this role, you have

• 5+ years building, shipping, and operating data infrastructure in production - not just writing pipelines, but owning their reliability, performance, and cost at scale
• Strong system design skills - you’ve written design docs, made trade-offs, and delivered the result
• Solid understanding of key metrics for data pipelines and experience building solutions to provide visibility to partner teams
• Deep proficiency in Python or Java/Kotlin, with strong opinions on testing, code quality, and maintainability in data-heavy codebases
• Production experience with the modern data stack, specifically:
• A cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Workflow orchestration (Airflow, Dagster, or Prefect) and IaC (Terraform)
• At least one streaming technology (Kafka, Flink, or Kinesis) - you understand when batch isn’t enough
• Data modeling and transformation frameworks (dbt)
• Built or improved observability for data systems - freshness monitoring, row-level quality checks, schema drift detection, and SLA dashboards that engineering and business teams actually trust
• Experience operating in a regulated environment (fintech, healthtech, or similar) where data governance, access control, and audit trails are non-negotiable

#LI-GC1 #LI-Onsite

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.

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.

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.

To thrive in this role, you have

• 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.

#LI-Hybrid #LI-GC1