Senior Data Engineer

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.

As a Senior Data Engineer at Rapsodo, you will own and evolve the data platform that powers analytics, reporting, and AI-driven insight across the company. You will design and maintain the cloud infrastructure, pipelines, and AI applications that turn raw product, financial, and customer data into trusted information for users across Product, Sales, Support, and Engineering.

You will inherit a mature, production-grade analytics stack spanning multiple data domains, a multi-layer warehouse, a BI platform serving users company-wide, and a growing set of AI applications. Looking ahead, you will build the next generation of AI agents on top of the warehouse — from internal analytics assistants to customer-facing applications such as an in-house AI customer support agent — as part of a small, high-leverage data team.

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.
• Optimise 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.

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.

Build AI-Powered Applications and Agents

• Build conversational analytics applications that let users ask plain-English questions and receive grounded, warehouse-backed answers.
• Design and build AI agents that leverage the data warehouse to power real business and customer-facing workflows — for example, an in-house customer support AI agent that draws on product usage, subscription, ticketing, and knowledge-base data to reduce support workload and resolution times.
• Architect end-to-end AI app stacks (serverless backends, LLM integrations, RAG, SQL generation, agentic tool use), with appropriate access controls, evaluation harnesses, and accuracy safeguards — especially when customer-facing.

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.
• Mentor interns and new hires; partner with another senior data engineer and an upcoming new hire as a small, high-trust team responsible for the entire data platform.
• Document architectures, runbooks, and incident learnings; contribute to engineering standards across code review, testing, and release processes.

Requirements

• 4+ years in data engineering, analytics engineering, or platform engineering, with meaningful ownership of production systems. Prior hands-on data engineering experience is essential.
• 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 optimising warehouse cost and performance on high-volume event data (partitioning, clustering, query tuning).
• Exposure to AI/LLM-powered applications and agents — RAG, tool use, SQL generation, conversational analytics. Interest in building customer-facing AI agents on top of warehouse data is especially valued.
• 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

At Rapsodo, you won’t just maintain pipelines — you’ll own the data platform and the AI applications that power every decision in the company, and you’ll build customer-facing AI agents used by athletes worldwide. If engineering rigour, ownership, and analytical impact that leads to real on-field results sound like your kind of role, apply now and help us build what’s next in sports technology.