Senior Data Engineer

Cutting Edge Music Tech - On-Demand Vinyl Records

We are seeking a Senior Data Engineer to help us redefine the music industry. We've invented a new technology to produce on-demand vinyl records and built a web platform for music creators to create and sell their products worldwide via our store at zero cost. We partner with leading record labels, streaming services, digital providers, distributors, and iconic global artists to build a global solution for physical media, but most importantly, we give small and emerging artists frictionless access to offer vinyl and CDs to their fans through our innovative solution and planned production/fulfilment centres in Europe, the USA, and Asia.

The vinyl market has grown over 20% yearly for the last 16 years, and CDs are growing again for the first time in two decades. By 2030, there will be nearly 200 million music creators worldwide (with AI accelerating this even further). Most would love to have their music on vinyl or CD for friends, family, and fans. Many would happily buy a record for around $30 if it's accessible without high costs or minimums, which our scalable on-demand tech makes possible. elasticStage delivers easy, affordable access to this booming opportunity.

We’re looking for a talented and driven Senior Data Engineer to design, build, and scale the data systems supporting our web platform and factory operations. As our first dedicated data hire, this is a hands-on, high-impact role. You’ll set standards for data architecture, reliability, and governance, ensuring pipelines are clean, monitored, and resilient. Your work will create a single source of truth for analytics, reporting, and future AI/ML use cases, shaping how data powers our business.

Come join us and help scale a fast-growing, high-profile industry disruptor!

Responsibilities:

• Design and build the foundational data stack from scratch data warehouse, ETL/ELT pipelines, and orchestration.
• Design and implement scalable data architecture using modern cloud services and managed data platforms.
• Build and maintain robust ETL/ELT pipelines for web data, factory telemetry, and third-party integrations.
• Select appropriate tools balancing cost, complexity, and team capabilities (e.g., choosing between Snowflake, BigQuery, or a simpler Postgres-based approach)
• Establish data modelling patterns that scale with the product.
• Work with Software Engineering to define data models, schemas, and best practices for data governance.
• Instrument event tracking across the product in collaboration with software and hardware engineers
• Provide analytics-ready datasets to enable BI, reporting, and stakeholder insights.
• Optimise cost, performance, and security across the data stack.