Data Engineer

FutureMedia is a dynamic, remote-first digital services company specializing in SaaS products, AI-powered solutions, and web and mobile application development. Our team brings together diverse digital experts who focus on innovation, problem-solving, and delivering impactful, technology-driven products for clients across global markets.

We design, develop, build, and analyze. We deliver impactful digital products and services — SaaS products, AI-powered solutions, web and mobile applications. As pioneers in the digital services space, we’re a team that knows how to innovate while having fun. Remote-first, but connection and collaboration are at our core.

About the Role

Our data is a strategic asset, and we are now building the infrastructure needed to unlock it. As an early member of our Data team, reporting to the Head of Data, you will design and own the pipelines, warehouse, and tooling that turn product signals into business intelligence.
You are joining at the ground floor: the data layer is being built now, so your decisions will shape it. We treat data as a product and hold a high bar for quality and reliability. The stack includes Python, SQL, dbt, Airflow or Prefect, Snowflake or BigQuery, Segment, Amplitude, AWS or GCP, Terraform, Looker or Metabase, Fivetran or Airbyte, GitHub Actions, and Kafka (planned).

What You’ll DoPipelines & Warehouse

• Design, build, and maintain scalable ELT/ETL pipelines ingesting data from product surfaces, payment providers, and third-party APIs
• Architect and manage our cloud data warehouse (Snowflake, BigQuery, or Redshift) from the ground up
• Implement orchestration workflows (Airflow, Prefect, or similar) for reliability and full observability

• Optimise warehouse performance and cost through query tuning, clustering, and sensible warehouse sizing

   

Transformation, Quality & Governance

• Develop clean, well-documented, testable transformation layers (dbt or equivalent), applying dimensional modelling principles
• Build and govern a canonical semantic layer so metric and entity definitions stay consistent across every BI tool and consumer
• Define and enforce data quality standards, SLAs, tests, and monitoring across critical datasets, and maintain a central data catalogue with clear lineage

• Build security and governance into the platform: RBAC, PII classification and masking, encryption, and retention and deletion workflows that support privacy requests

   

Product & Engineering Partnership

• Partner with Product and Growth to instrument event tracking (Segment, Amplitude, or similar) and deliver datasets that power funnels, subscription metrics (MRR, churn, LTV), and A/B test reporting
• Collaborate with Software Engineers on data contract design, API logging, and Change Data Capture where needed

• Champion DataOps practices: version control, CI/CD for data, and documentation-as-code

   

What We’re Looking For (Must-Have)

• 3+ years of professional experience as a Data Engineer or in a closely related role
• Strong command of SQL and at least one of Python or Scala for data processing
• Hands-on experience with a modern cloud data warehouse (Snowflake, BigQuery, or Redshift)
• Experience with workflow orchestration tools (Airflow, Prefect, Dagster, or equivalent)
• Prior experience at a B2C SaaS, subscription, or marketplace business, with a grasp of funnels, churn, and LTV
• Solid understanding of data modelling principles and experience with dbt or a similar transformation framework
• Proven ability to design and scale batch and/or streaming data pipelines in production
• A strong sense of data ownership — you treat data as a product and hold a high bar for quality and reliability
• A clear communicator who can translate technical decisions into business value for non-technical stakeholders

• Comfortable handling personal data responsibly, applying access controls and privacy-aware practices

   

Nice to Have

• Familiarity with product analytics tools such as Segment, Amplitude, or Mixpanel
• Experience building or contributing to a semantic layer or canonical data model
• Experience with streaming platforms (Kafka, Kinesis) for real-time use cases
• Exposure to ML infrastructure or feature stores, a plus as we expand into AI products
• Experience setting up a data platform from scratch in a scale-up environment

Working Conditions

• Competitive compensation
• Remote-first culture with flexible working hours 
• 22 paid vacation days + local national holidays 
• Opportunity to shape the excellence across a growing engineering organisation 
• Modern stack, scalable products, and meaningful technical challenges