Senior Data & AI Engineer

About Rapsodo

Rapsodo is a Sports Technology company with offices in the USA, Singapore, Turkey, Malaysia & Japan. We build data-driven sports analytics products used by athletes worldwide — from Major League Baseball players to golf enthusiasts — and by the coaches who train them. We are looking for team players who will help us deliver state-of-the-art solutions as part of Team Rapsodo.

Overview

As a Senior Data & AI Engineer at Rapsodo, you will own the data platform that powers analytics, reporting, and decision-making across the company — and you will drive development of the agentic workflows built on top of it. This is a data engineer's role first: the custodianship of a production-grade warehouse spanning multiple domains, a multi-layer star schema, and a BI platform serving users company-wide. On top of that foundation, you will lead the design and delivery of agentic workflows that turn trusted data into informed, reliable action.

What You’ll Do

• Own and Operate the Cloud Data Platform

• Maintain cloud analytics infrastructure as code, treating the IaC repository as the source of truth for every cloud resource.

• Operate a modern data stack spanning workflow orchestration, container management, ingestion, BI, and serverless compute; lead upgrades and patches with minimal disruption.

• Operate the company-wide BI platform, including SSO integration and supporting dashboard workflows at scale.

• Optimize warehouse cost and performance through partitioning, clustering, and query tuning; set up alerting across pipelines, connectors, and scheduled queries.

• Enforce least-privilege access, rotate credentials on schedule, maintain backups, and keep warehouse documentation current.

• Act as the final word on data quality and trust — every agent, dashboard, or automated workflow built on the warehouse is only as reliable as the custodianship behind it.

• Build and Evolve Data Pipelines & Integrations

• Develop transformation repositories on a multi-layer (raw → staging → curated) star schema, with incremental hash-based loading that keeps refreshes performant on very large datasets.

• Author and maintain orchestration DAGs covering ingestion, transformation, retries, scheduling, and alerting; diagnose incidents with strong root-cause discipline.

• Build and maintain ingestion for real-time and batch sources — database CDC, ERP, identity provider syncs, and SaaS connectors across e-commerce, payments, support, marketing, and marketplaces.

• Lead new ingestion projects end-to-end (e.g. product event analytics, device telemetry) and drive the design of a unified semantic layer across product, CRM, billing, marketing, and support data — the layer that both humans and agents will query against.

• Drive Agentic Workflow Development

• Own the roadmap for AI agents built on top of the data warehouse — deciding which workflows justify an agent, what "grounded and trustworthy" means for each, and how they're deployed and monitored.

• Design and build production agentic systems that read and act on warehouse data, complementing business workflows and adding value.

• Architect end-to-end AI app stacks — serverless backends, LLM integrations, RAG, SQL generation, tool-calling/agentic orchestration, memory and context management — with access controls, evaluation harnesses, and accuracy safeguards appropriate to customer-facing use.

• Establish engineering standards for agentic development at Rapsodo: observability (logging, tracing, quality monitoring) for AI systems, cost/latency-aware model and fallback strategy, safe and repeatable CI/CD for AI services, and guardrails against hallucination or unsafe autonomous action.

• Push the organization's use of the warehouse forward — identify where an agent, a reporting dashboard or another solution, is the right answer to a business problem, and make the case for it.

• Partner with Stakeholders and the Team

• Support stakeholder reporting across Product, Sales, Support, Engineering, and leadership; own quarterly business processes that depend on the warehouse (e.g. commission calculations) in partnership with Sales and Finance.

• Validate ingested data against business sources and lead investigations into discrepancies until they are resolved — the same rigor applies whether the consumer is a dashboard or an agent.

• Manage data team and support other departments.

• Document architectures, runbooks, agent evaluation results, and incident learnings; contribute to engineering standards across code review, testing, and release processes.

Requirements

Requirements

• 6+ years in data engineering, analytics engineering, or platform engineering, with meaningful ownership of production systems and warehouse architecture end-to-end. Deep, hands-on data engineering experience is non-negotiable — this role leads with data custodianship, not AI engineering alone.
• Demonstrated experience designing and shipping production AI agent systems — tool-calling architectures, RAG pipelines, context/memory orchestration, and evaluation frameworks — experience beyond prototyping with an LLM API is a plus.
• Bachelor's degree in Computer Science, Engineering, or a related discipline; master's a plus.
• Strong hands-on experience with a major cloud data platform (e.g. GCP, AWS, or Azure) — managed compute, serverless functions, container workloads — and Infrastructure-as-Code tooling.
• Deep proficiency in SQL and Python; experience with a SQL transformation framework (Dataform, dbt, or equivalent) and container orchestration (Kubernetes or equivalent).
• Experience building ingestion pipelines — batch and real-time (e.g. CDC) — and integrating SaaS sources with proper credential hygiene; familiarity with BI platforms and supporting non-technical users at scale.
• Experience optimizing warehouse cost and performance on high-volume event data (partitioning, clustering, query tuning).
• Practical experience with LLM APIs (OpenAI, Claude, or similar), vector databases/embedding pipelines, and evaluating LLM quality, hallucinations, and failure modes in production.
• A track record of making the call on when an AI agent is — and isn't — the right solution to a business problem, and owning that decision through to a reliable, monitored production system.
• Strong security mindset; comfort partnering with business stakeholders on cross-functional deliverables; excellent communication, documentation, and ownership in fast-paced environments.
• Interest in sports technology is a plus.

Benefits

• Why Rapsodo?

• At Rapsodo, you won't just build agents on someone else's data platform — you'll own the platform itself and decide how AI gets built on top of it. This role is built for someone who wants both: the discipline of being the trusted custodian of a company's data, and the license to drive where agentic AI takes the organization next. If you want to shape both the foundation and the frontier, apply now.