The ad says“Orchestrate workflows using Airflow, Dataflow, Glue”
The ad says“CI/CD pipelines using tools like GitHub Actions, ArgoCD”
The ad says“Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)”
The ad says“Solid grasp of cloud data services (AWS or GCP)”
The ad says“Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)”
The ad says“Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)”
The ad says“Deep experience with Spark and modern data platforms (Databricks / Snowflake)”
The ad says“Orchestrate workflows using Airflow, Dataflow, Glue”
The ad says“Data testing and quality frameworks (dbt, Great Expectations, Soda)”
The ad says“Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)”
The ad says“Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub”
The ad says“Solid grasp of cloud data services (AWS or GCP)”
The ad says“CI/CD pipelines using tools like GitHub Actions, ArgoCD”
The ad says“Orchestrate workflows using Airflow, Dataflow, Glue”
The ad says“Data testing and quality frameworks (dbt, Great Expectations, Soda)”
The ad says“Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub”
The ad says“Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub”
The ad says“Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)”
The ad says“Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)”
The ad says“Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)”
The ad says“Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)”
The ad says“Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub”
The ad says“Infrastructure-as-code experience (Terraform, Pulumi)”
The ad says“Strong Python and SQL”
The ad says“Deep experience with Spark and modern data platforms (Databricks / Snowflake)”
The ad says“Data testing and quality frameworks (dbt, Great Expectations, Soda)”
The ad says“Deep experience with Spark and modern data platforms (Databricks / Snowflake)”
The ad says“Strong Python and SQL”
The ad says“Infrastructure-as-code experience (Terraform, Pulumi)”
This is a hands-on principal 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
If you enjoy solving hard data problems, shaping modern architectures (data mesh, data products, contracts), and delivering real outcomes — this is your lane
Lead Platform & Architecture Design
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
Build Modern Data Platforms
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
Develop High-Performance Pipelines
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
Work at Scale
Process and transform large datasets using Spark and Flink
Design systems that perform in production - not just on paper
Own Data Storage & Performance
Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)
Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)
Cloud, Security & Governance
Implement secure, compliant data solutions with security by design
Embed governance without killing developer velocity
Consult and Influence
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
Technical Leadership & Quality
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
You’ll work on interesting, high-impact problems
You’ll build modern platforms, not maintain legacy mess
You’ll be surrounded by senior engineers who actually know their craft
You’ll have autonomy, influence, and room to grow
If you’re a senior data engineer who wants to build properly, think clearly, and deliver real outcomes - we should talk
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
Preferred experience
Deep experience with Spark and modern data platforms (Databricks / Snowflake)
Solid grasp of cloud data services (AWS or GCP)
Strong Python and SQL
Comfortable balancing trade-offs between performance, cost, and complexity
Demonstrated ownership of large-scale data platform architectures
Strong data modelling skills and architectural decision-making ability
Strong data modelling capability and architectural judgement
Built and operated large-scale data pipelines in production
Comfortable with multiple storage technologies and formats
