Staff Software Data Engineer - Credit Karma

• Design, build, and maintain high-throughput, low-latency data frameworks used across Credit Karma’s engineering organization, including ETL templates, persistence libraries, and streaming data pipelines
• Develop and extend Scala-based microservices and frameworks built on Finagle, Akka Streams, and gRPC that process petabytes of data daily
• Build and optimize cloud-native data pipelines on Google Cloud Platform using Dataflow (Apache Beam), Pub/Sub, BigQuery, and Spanner
• Own and evolve our Kafka-based streaming infrastructure - designing producers, consumers, and connectors that handle hundreds of terabytes of events per day with strict latency and durability guarantees
• Build persistence frameworks that provide a unified, type-safe API for reading and writing across Spanner, MySQL, and BigQuery
• Design and implement encryption, decryption, and fine-grained access control capabilities as reusable framework features, ensuring compliance with data governance requirements
• Create self-service developer tooling - CLI tools, templates, and onboarding automation - that reduces the time for other teams to adopt the data platform from weeks to hours
• Drive technical design through architecture reviews and Technical Design Documents (TDDs), influencing decisions across the broader Data & AI organization
• Participate in on-call rotations and build observability (dashboards, alerting, metrics) into every system you ship

What’s Great About the Role

• You will own foundational infrastructure - the frameworks you build are the building blocks that every data pipeline and product feature at Credit Karma depends on
• You will work at real scale - hundreds of terabytes per day, millions of consumer profiles hydrated in real time, and strict SLAs that demand engineering rigor
• You will shape developer experience - designing the APIs, SDKs, and tooling that hundreds of engineers across the company interact with daily
• You will solve hard, novel problems - from building MySQL CDC pipelines with cloud-native encryption to designing Apache Beam framework SDKs that support both Java and Python
• You will be part of a high-impact, collaborative team with a strong culture of technical ownership and continuous learning

Do you have experience in Version control systems?, * 7+ years of professional software engineering experience building backend services and data infrastructure in Scala, Java, or a similar JVM language

• 7+ years of experience designing and operating high-throughput, low-latency distributed systems that process data at petabyte scale
• 3+ years of experience with streaming and messaging platforms such as Apache Kafka, Google Pub/Sub, or equivalent
• 3+ years of experience building data pipelines on a major cloud platform (GCP, AWS, or Azure), including services like Dataflow, BigQuery, Spanner, or their equivalents
• Professional experience with RPC frameworks such as Finagle, gRPC, or Akka for building production-grade service-to-service communication
• Strong understanding of software engineering best practices including CI/CD, version control (Git), code review, and automated testings, * Experience building reusable frameworks, SDKs, or platform libraries consumed by other engineering teams - you think about developer experience as a product
• Experience with Apache Beam (Dataflow) including custom transforms, side inputs, windowing strategies, and pipeline optimization
• Experience with Change Data Capture (CDC) patterns, particularly MySQL binlog-based replication to analytical stores
• Experience with data encryption at rest and in transit, including key management (KMS/GSM), SPIFFE/mTLS, and certificate authority integration
• Experience with schema management, data governance, and data quality frameworks in a large-scale production environment
• Familiarity with infrastructure-as-code, Kubernetes (GKE), and container-based deployment models
• Track record of mentoring engineers and driving technical alignment across teams through design documents and architecture reviews