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Salary: USD 190,000 - 230,000 / annual
Human Interest is a high-growth fintech company on a mission to make retirement benefits accessible to small and medium-sized businesses across the US. The company is backed by major investors including BlackRock, TPG, and SoftBank, and has raised approximately $500 million to date.
As a Senior Software Engineer on the Data Platform team, you will own the design, build, and operation of distributed systems and services that power Human Interest's data platform. This is a high-ownership role where your architectural decisions will directly impact the company's capabilities at a pivotal growth stage.
You will be responsible for designing and operating the distributed services and compute infrastructure behind the data platform, including containerized workloads on AWS ECS, autoscaling, and orchestration layers. You'll own end-to-end design and development of scalable data pipelines and data-moving services, from ingestion through transformation and delivery. Key responsibilities include improving how event-sourced data moves from production systems into the data infrastructure, participating in on-call for the data platform, and owning production issues in distributed data systems end-to-end.
Security and compliance are critical: you will ensure participant and employer data is handled securely as it moves through the platform, including PII classification and masking, role-based access controls, encryption and key management, lineage tracking, and audit trails required for SOC 2 and regulatory compliance.
You will lead the technical direction and evolution of the data platform toward an AI-first infrastructure, designing for AI consumption, unstructured data access, and integration with AI tooling from the ground up. You'll build the interfaces that data engineers, analysts, and AI systems use, and mentor engineers and analysts to raise the technical bar across the team.
The Data Platform team owns the data stack from ingestion and orchestration through transformation, governance, and delivery to BI tools. The current stack includes Snowflake, dbt, Airflow, Meltano, and AWS. The team is small, highly collaborative, and embedded within the HI Tech organization, working closely with Analysts, Finance, Product, and engineering teams.
The team actively uses AI tools as part of the development workflow, and this role is expected to help drive and expand that practice.
**Requirements:**
**Required:**
- 5+ years of experience building and operating production software systems, with significant time spent on data-intensive systems such as data pipelines, data streaming platforms, and data infrastructure
- Demonstrated experience designing distributed systems and services, with the ability to reason clearly about software concepts like concurrency, backpressure, idempotency, partial failure, and the tradeoffs between them
- Hands-on experience running containerized workloads in production on AWS including scaling, resource sizing, and performance and cost tuning under real load
- The ability to independently own and improve complex production systems, alongside the software engineering practices that go with them: automated testing, code review, CI/CD, and infrastructure as code such as Terraform
- Experience with workflow orchestration at scale, including Airflow or equivalent tools
- A strong desire to leverage AI tools and workflow automation as the primary way work gets done, not just to augment work
**Preferred:**
- Hands-on experience with event-sourced or append-only log systems, or with change data capture and streaming platforms (e.g. Kafka or Kinesis)
- Working knowledge of cloud data warehouse technologies, including Snowflake or equivalent, covering access control, performance tuning, and cost management
- Experience getting machine learning models into production and keeping them healthy there, particularly the engineering side of ML in production rather than model development itself (e.g. serving infrastructure and monitoring)
- Experience with data lakehouse architectures and open table formats such as Apache Iceberg or Delta Lake
- Experience building data infrastructure to support AI-driven data access, such as exposing data assets via MCP, implementing governance frameworks for AI data access, and curating, monitoring, and evaluating the quality of AI-generated query responses
- Background in fintech, financial services, or another highly regulated or compliance-driven industry
