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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Data Platform - **Company:** Human Interest Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $190,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Artificial Intelligence, Airflow, Amazon Web Services, Apache HTTP Server, Automation of Tests, Backup Devices, Code Review, Continuous Integration, Data Infrastructure, Data Security, Digital Assets, Distributed Data Store, Distributed Systems, Key Management, Machine Learning, Operational Databases, Performance Tuning, Data Streaming, Unstructured Data, User-Centered Design, Workflow Management Systems, Snowflake, Change Data Capture, AWS ECS, Data Lakes, Infrastructure Automation Frameworks, Apache Kafka, Terraform, Data Pipelines - **Published:** October 6, 2026 - **Apply:** https://www.builtincolorado.com/job/senior-software-engineer-data-platform/11518426?handler=ApplyRedirect ## About the Role * 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. We are interested in 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. ## Description As a Senior Software Engineer on the Data Platform team, you will build and operate the distributed systems and services that Human Interest's data platform runs on, at a pivotal moment in Human Interest's growth. This role exists to own the infrastructure and compute layer that moves data reliably at scale. You will drive the evolution of our data platform toward an architecture that scales for AI consumption and for Human Interest's next growth stage. This is a software engineering role in the data domain. You will spend your time designing services, reasoning about failure modes, and tuning distributed compute rather than authoring analytics models. We are looking for engineers who have built production systems and moved into the data world, and who want to keep building systems there. This is a high-ownership role where your architectural decisions will have a direct and visible impact on Human Interest's capabilities. About the team The Data Platform team at Human Interest owns the data stack from ingestion and orchestration through transformation, governance, and delivery to BI tools. Our stack currently includes Snowflake, dbt, Airflow, Meltano, and AWS. We're a small, highly collaborative team embedded within the HI Tech organization, working closely with Analysts, Finance, Product, and engineering teams to ensure that the data people rely on is trustworthy and accessible. You'll shape how we evolve our architecture towards AI-consumption and to set the standard for how production data systems are built, deployed, and operated at Human Interest. The team actively uses AI tools as part of our development workflow, and we expect this role to help drive and expand that practice across the team. What you get to do every day * Design, build, and operate the distributed services and compute infrastructure behind our data platform, including containerized workloads on AWS ECS, autoscaling and resource sizing, and the orchestration layer that coordinates them. * Own end-to-end design and development of scalable data pipelines and data-moving services, from ingestion and orchestration through transformation and delivery. * Improve how event-sourced data moves from our production systems into our data infrastructure and back out to other systems. * Participate in on-call for the data platform, and own production issues in distributed data systems end to end. * Ensure participant and employer data is handled securely as it moves through the platform, including PII classification and masking, role-based access controls and least-privilege patterns, encryption and key management, lineage, and the audit trails our SOC 2 and regulatory obligations depend on. * Lead the technical direction and evolution of our data platform as we move toward an AI-first data infrastructure. You will design for AI consumption, unstructured data access, and integration with AI tooling from the ground up, including the pipelines and infrastructure that support model and AI workloads running in production. * Build the interfaces that data engineers, analysts, and AI systems use, and the systems that move curated data back out of the warehouse into production services and downstream tools. * Mentor engineers and analysts and raise the technical bar across the team, setting the standard for testing, code review, and operational readiness on the data stack.