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Come join Intuit's Consumer Group as a Staff Software Engineer - Data and help build Consumer Data Plane using Credit Karma and Turbotax Data to enable the data layer for AI-Native Experiences & Agents, Growth marketing, In-product personalization, Expert, 3rd Party surfaces, and Agentic Browsers. 

The team owns the data platform at Credit Karma - from service emission to data lake landing to consumer hydration. We build the frameworks, pipelines, and persistence layers that every product team depends on to move data reliably at scale. Our systems ingest hundreds of terabytes per day across Kafka, Pub/Sub, and Dataflow, setup Data Governance, persist to Spanner and BigQuery, and serve low-latency reads to real-time product experiences through our Consumer Data Plane.

This is a foundational engineering role - you will build the frameworks and infrastructure that other engineers across Credit Karma use to ship data-intensive features. If you care about developer experience, system reliability, and solving hard distributed systems problems at scale, this team is for you.

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
•  Build agentic systems, semantic search, knowledge graph and personalization to disrupt how consumers do their taxes, money and personal finance using Consumer Data Plane
•  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
• 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

Qualifications

• 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, 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, ..) to integrate our data with generative AI experiences 
• 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

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: $202,500 - $274,000 depending on location.