Senior Data Engineer

• This is a hands-on senior engineering role, not an architecture-only seat and not a support function. You’ll be responsible for technical direction, platform design and architectural decision-making
• You’ll design and build greenfield data platforms, real-time pipelines, and data products for clients who are serious about using data properly. You’ll work in small, high-calibre teams and operate close to both the problem and the client
• Own the end-to-end architecture of modern, cloud-native data platforms
• Design scalable data ecosystems using data mesh, data products, and data contracts
• Make high-impact architectural decisions across ingestion, storage, processing, and access layers
• Ensure platforms are secure, compliant, and production-grade by design
• Design and deliver cloud-native data platforms using Databricks, Snowflake, AWS, and GCP
• Apply modern architectural patterns: data mesh, data products, and data contracts
• Integrate deeply with client systems to enable scalable, consumer-oriented data access
• Build and optimise batch and real-time pipelines
• Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub
• Orchestrate workflows using Airflow, Dataflow, Glue
• Process and transform large datasets using Spark and Flink
• Design systems that perform in production - not just on paper
• Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)
• Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)
• Implement secure, compliant data solutions with security by design
• Embed governance without killing developer velocity
• Work directly with clients to understand problems and shape solutions
• Translate business needs into pragmatic engineering decisions
• Act as a trusted technical advisor, not just an order taker
• Set engineering standards, patterns, and best practices across teams
• Review designs and code, providing clear technical direction and mentorship
• Raise the bar on data quality, testing, observability, and operational excellence

Benefits

• Employee assistance programme
• Generous parental leave: Three months’ fully paid primary carer leave and one month fully paid secondary carer leave, subject to a minimum 12 month tenure
• Flexible working: Want to work from home or leave early to pick up the kids? Need to start early some days, later on others? No problem!
• “Your day”: One day per year, in addition to annual leave days that you can take off with no explanation required
• Upskill - Learning and development: We all love continually learning and have a generous budget for you to upskill, join conferences, get certified and qualified

• If you enjoy solving hard data problems, shaping modern architectures (data mesh, data products, contracts), and delivering real outcomes — this is your lane
• Deep experience with Spark and modern data platforms (Databricks / Snowflake)
• Solid grasp of cloud data services (AWS or GCP)
• Strong Python and SQL
• Demonstrated ownership of large-scale data platform architectures
• Comfortable with multiple storage technologies and formats
• If you’re a senior data engineer who wants to build properly, think clearly, and deliver real outcomes - we should talk
• Data testing and quality frameworks (dbt, Great Expectations, Soda)
• Strong data modelling skills and architectural decision-making ability
• Built and operated large-scale data pipelines in production
• CI/CD pipelines using tools like GitHub Actions, ArgoCD
• Strong data modelling capability and architectural judgement
• Strong consulting instincts — able to challenge assumptions and guide clients toward better outcomes
• Experience in consulting or professional services environments
• Infrastructure-as-code experience (Terraform, Pulumi)
• Comfortable mentoring senior engineers and influencing technical culture
• Comfortable balancing trade-offs between performance, cost, and complexity