Staff Data Engineer - Credit Karma

Responsibilities

• 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), Flink, Spark, 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 organization
• Participate in on-call rotations and build observability (dashboards, alerting, metrics) into every system you ship

Requirements

• 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 testing
• 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
• Knowledge of data governance, data catalog, data quality and lineage management frameworks in a large-scale production environment
• Knowledge of AI/ML and GenAI technologies — LLMs, RAG, Semantic Search (e.g Vertex AI search, AWS cloud search) and Knowledge Graph (e.g neo4j, ..)
• 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

Qualifications

• Professional experience in software engineering
• Experience with Scala, Java, or similar JVM languages
• Experience with distributed systems and data processing at petabyte scale
• Experience with streaming and messaging platforms
• Experience with cloud platforms and data pipelines
• Experience with RPC frameworks
• Experience with Apache Beam (Dataflow)
• Experience with Change Data Capture (CDC) patterns
• Experience with data encryption
• Experience with data governance and management
• Experience with AI/ML and GenAI technologies
• Experience with infrastructure-as-code and Kubernetes
• Experience with mentoring and driving technical alignment