Senior Data Analytics Engineer

We're looking for a Senior Data Analytics Engineer who lives and breathes data—someone who combines strong engineering skills with analytical depth. You'll join a small, high-impact team where you don't just analyze data, you build the systems and pipelines that make analytics possible. This isn't a dashboards-and-decks role—you'll engineer solutions across the full analytics stack, from designing reliable data pipelines in BigQuery and MariaDB to building internal tools and APIs that put insights into the hands of the business.

Day to day, you'll write sophisticated SQL in BigQuery and MariaDB, architect and build automation and tooling in Python and FastAPI, and use AI-powered development tools like Windsurf, Gemini, and Claude as a core part of how you work. You need to understand data architecture deeply—how information flows from source systems through pipelines into the warehouse, how schemas are designed, and how to build infrastructure that scales and stays maintainable.

Communication is a big part of this role. You'll work closely with stakeholders across the business, translating complex data into clear, actionable insights. But equally important is your ability to engineer robust, repeatable solutions—not one-off analyses. We need someone who can own their domain end-to-end: define the right questions, build the data infrastructure, deliver the analysis, and present it in a way that drives decisions. If you're self-sufficient, ambitious, always looking to improve, and you have a public GitHub profile that shows what you're capable of—we want to talk to you.

Key Responsibilities

• Design, build, and maintain analytical pipelines and reporting solutions on BigQuery and MariaDB
• Write sophisticated, performant SQL to extract insights from large-scale datasets
• Leverage AI and machine learning features to augment analytics workflows and deliver smarter outputs
• Use AI-powered development tools (Windsurf, Gemini, Claude, Cursor) to accelerate your workflow and deliver at a higher level
• Develop internal tooling and automation using Python, FastAPI, and modern frameworks
• Communicate findings clearly to technical and non-technical stakeholders—translate data into action
• Understand and document data architecture, lineage, and how information flows across systems
• Collaborate on business process analysis and process design improvements driven by data
• Proactively identify opportunities for deeper analysis, better tooling, and operational improvements

Requirements & Qualifications

• 7+ years of professional experience in data analytics engineering, data engineering, or a related field
• Expert-level SQL - you think in joins, window functions, and CTEs; BigQuery and MariaDB experience strongly preferred
• Strong Python and/or JavaScript skills - comfortable building pipelines, internal tools, APIs, and automation; not just scripts
• JavaScript experience - ability to build data-facing web applications and internal dashboards
• Deep understanding of data architecture - how databases are structured, how data flows, how schemas evolve
• Hands-on experience with AI/ML - you stay current with the latest capabilities (LLMs, generative AI, embeddings) and know how to apply them practically
• Proficiency with AI-powered development tools - Windsurf, Gemini, Claude, Cursor, or similar; you use AI to multiply your output, not as a crutch
• Excellent communication skills - you can explain complex analysis to anyone in the room
• Self-sufficient and self-managing - you own your work end-to-end without needing to be told what to do next
• Ambitious and continuously improving - you seek out new skills, tools, and techniques on your own
• A public GitHub profile with examples of your work (personal projects, contributions, experiments)

Nice to Have

• FastAPI experience for building internal APIs and services
• Background in business process design or operational analytics
• Exposure to multiple business departments (Finance, Marketing, Sales, Operations, Engineering) - you understand how data flows across an organization, not just within one silo
• Understanding of Docker and Kubernetes - containerization and cluster orchestration experience is a big plus