Data Science Engineer Jobs at Intuit with Visa Sponsorship
Data Science Engineer jobs at Intuit involve building the models and pipelines powering products used by millions of small businesses and consumers. The company has a consistent track record of sponsoring work visas for technical roles in this function, supporting candidates across multiple visa categories from initial offer through long-term status.
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About the Team
The teams own the end-to-end data path at Credit Karma — from service emission to datalake 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, persist to Spanner and BigQuery, and serve low-latency reads to real-time product experiences through our Unified Consumer Profile (UCP) platform.
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.
- 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
Minimum Basic 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
Qualifications
Preferred Qualifications
- 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
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.
Overview
About the Team
The teams own the end-to-end data path at Credit Karma — from service emission to datalake 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, persist to Spanner and BigQuery, and serve low-latency reads to real-time product experiences through our Unified Consumer Profile (UCP) platform.
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.
- 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
Minimum Basic 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
Qualifications
Preferred Qualifications
- 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
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.
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Get Access To All JobsTips for Finding Data Science Engineer Jobs at Intuit
Align your portfolio to Intuit's product stack
Intuit's Data Science Engineer roles sit at the intersection of ML engineering and product analytics. Projects demonstrating experience with recommendation systems, fraud detection, or large-scale personalization map directly to what their hiring teams evaluate.
Verify your degree field matches specialty occupation criteria
USCIS requires your degree to directly relate to the role for H-1B approval. For Data Science Engineer positions, a degree in computer science, statistics, or applied mathematics is the safest foundation. A business degree alone rarely satisfies the specialty occupation standard.
Target Intuit's TurboTax and QuickBooks engineering teams
Intuit's two flagship product lines generate the highest volume of Data Science Engineer openings. Filtering your search by these business units surfaces roles with the clearest sponsorship pathways, since those teams have established headcount planning and visa filing workflows.
Use Migrate Mate to filter Intuit's open roles by visa type
Intuit sponsors multiple visa categories for Data Science Engineers. Migrate Mate lets you filter current openings by the visa types you hold or need, so you can prioritize applications to roles where your status is already supported.
Start the H-1B process before your OPT STEM extension expires
F-1 OPT STEM extensions give you 24 months of additional work authorization. If your lottery registration falls in the final year of that extension, a gap between OPT expiry and H-1B start date is possible. Discuss cap-gap coverage with your employer's immigration counsel early.
Confirm PERM sponsorship scope during the offer stage
Intuit sponsors EB-2 and EB-3 Green Cards, but PERM timelines at DOL currently run 12 to 18 months before USCIS review begins. Ask your recruiter explicitly whether the role includes an employer-sponsored Green Card as part of the compensation package before you sign.
Frequently Asked Questions
Does Intuit sponsor H-1B visas for Data Science Engineers?
Yes, Intuit sponsors H-1B visas for Data Science Engineer roles. The company participates in the annual H-1B lottery each April and covers associated filing fees as part of its standard immigration support. If you're on F-1 OPT, Intuit typically begins the H-1B visa registration process while you're still in your authorized period, and cap-gap rules protect your status if your OPT expires before October 1.
How do I apply for Data Science Engineer jobs at Intuit?
Applications go through Intuit's careers portal at intuit.com/careers. Search for Data Science Engineer roles and filter by location, since most positions are based in Mountain View, San Diego, or Atlanta. Tailor your resume to reflect ML engineering experience relevant to Intuit's product domains. You can also browse current openings filtered by visa type on Migrate Mate before applying directly through Intuit's site.
Which visa types does Intuit commonly use for Data Science Engineer roles?
Intuit sponsors H-1B, E-3 visa, TN visa, and F-1 OPT and CPT for Data Science Engineer positions, and supports EB-2 and EB-3 Green Card sponsorship for longer-term employment. Australian citizens benefit from the E-3 visa category, which has no lottery and allows two-year renewable periods. Canadian and Mexican nationals may qualify under the TN visa category, which permits relatively fast processing at the port of entry.
What qualifications does Intuit look for in Data Science Engineer candidates?
Most Data Science Engineer roles at Intuit require a bachelor's degree or higher in computer science, statistics, or a closely related quantitative field. Practical experience with Python, Spark, and cloud-based ML infrastructure is consistently listed across job descriptions. Roles on product teams like TurboTax or QuickBooks often emphasize experimentation frameworks and A/B testing in addition to model deployment experience.
How long does the visa sponsorship process typically take for a Data Science Engineer at Intuit?
Timeline depends on visa type. H-1B sponsorship follows the April lottery cycle with an October 1 start date, so plan for a six-month gap between offer and start if you need a new cap-subject petition. E-3 and TN visas can often be processed within two to four weeks through consular appointments. PERM-based Green Card sponsorship through DOL takes 12 to 18 months before reaching USCIS, so the full EB-2 or EB-3 process spans several years for most applicants.