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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Data Infrastructure - **Company:** AI Powered LLC - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Databases, Data Infrastructure, Extract Transform Load (ETL), DevOps, Python (Programming Language), Operational Databases, SQL Databases, Teradata SQL, Snowflake, Apache Spark, Electronic Medical Records, AWS Glue, Data Analytics, Amazon Redshift - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/software-engineer-data-infrastructure-sapiom-8032473 ## About the Role * Demonstrated track record - 5+ years - transforming raw data into governed, well-documented, production-ready datasets that business teams can trust and use * Deep hands-on experience building and deploying production data pipelines using SQL, Python, Spark, AWS Glue, EMR, DBT, and Airflow * Strong command of MPP databases - Snowflake, AWS Redshift, or Teradata - with 3+ years of hands-on production use * Proven partnership record with Engineering, Analytics, Data Science, and DevOps teams - someone who treats cross-functional relationships as core to the job, not peripheral to it * Architectural instincts - able to design schemas and systems that scale gracefully, not just handle today's load * Comfort operating in an on-call rotation - including incident response outside regular working hours when the pipeline demands it * Clear communicator who can translate complex data infrastructure decisions into plain-language insights for both technical and non-technical stakeholders ## Description This is a foundational infrastructure role at a company where the data layer isn't a back-office function - it's the nervous system of a payments platform processing every agent transaction, policy decision, and risk signal in real time. The right person thrives on ownership, has strong opinions about data quality and governance, and moves with the urgency of someone who knows that bad data costs more than bad code. As an early data engineer, you'll define not just the pipelines but the standards, architecture, and culture of data at Sapiom. What You Will Do You'll own Sapiom's data infrastructure end-to-end - designing and scaling ETL pipelines, defining schemas that survive 10x growth, and building the governance and quality frameworks that make data trustworthy across the company. You'll architect standardized data models that enable self-serve AI-powered insights, giving Analytics, Data Science, and product teams the visibility they need to move fast without coming to you for every query. The mandate is broad: pipelines, quality, security, observability, and the cross-functional partnerships that keep it all running. Responsibilities * Build, scale, and optimize production-quality ETL pipelines - owning the full lifecycle from ingestion through availability, with clear quality and SLA standards * Design data schemas and architect for scale - anticipating 10x data growth and building models that don't require rework when it arrives * Own data quality, governance, security, and schema design across the platform - setting the standards and making sure they hold * Develop standardized, self-serve data models that enable AI-powered analytics - reducing friction for partner teams and eliminating one-off data pulls * Instrument pipeline observability and surface key health metrics to Analytics, Data Science, and DevOps - proactively surfacing issues before they become incidents * Partner closely with Data Science, Analytics, and DevOps - operating as a force multiplier across teams, not a bottleneck ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)