Data engineer
Summary
A Modern Data Engineer building and deploying cloud-native data platforms using Python, Spark, Databricks, Kafka, Flink, and AWS. Responsibilities include developing batch/streaming solutions, using CI/CD, Git, and modern DevOps practices.
This role is for a
Modern Data Engineer
with strong software engineering skills who can build and deploy cloud-native data platforms.
Key Requirements Software Engineering
Strong Python development skills (primary programming language)
Solid understanding of software engineering principles and clean coding practices
Experience working within the Software Development Life Cycle (SDLC)
Proficient with Git and version control
Experience with CI/CD pipelines and modern Dev Ops practices
Ability to solve problems through code, not just SQL
Hands-on experience with modern data engineering technologies, including:
Spark / Py Spark
Databricks
Kafka
Flink
Data Lake architectures
Experience building both batch and streaming data solutions
Practical experience delivering solutions on at least one major cloud platform:
Amazon Web Services (AWS)
Relevant services may include:
Data processing and analytics tools (Big Query, Synapse, Redshift, Databricks, Dataflow, Glue, EMR, Dataproc)
Event streaming platforms (Pub/Sub, Event Hubs, Kinesis)
Container platforms (GKE, AKS, EKS)
Dev Ops & Deployment Experience with:
Docker
Infrastructure as Code (Terraform or similar)
Monitoring and observability tools
Experience should be gained through real production projects, not certifications alone.
Additional Valuable Skills
Data modelling (relational and dimensional)
Data warehousing concepts
Modern orchestration and data platforms such as Databricks, Kafka and Microsoft Fabric
Ideal Candidate We're looking for a modern data engineer with a software engineering mindset. Candidates should have proven experience delivering cloud-based data solutions using Python, Spark, CI/CD, Git and modern data platforms. Experience limited to traditional SQL, SSIS or ETL development without modern engineering practices is unlikely to meet current client requirements.
What We Value
Continuous learning and professional development
Ability to contribute immediately within a modern engineering environment
Client-facing consulting experience is advantageous
Modern Data Engineer
with strong software engineering skills who can build and deploy cloud-native data platforms.
Key Requirements Software Engineering
Strong Python development skills (primary programming language)
Solid understanding of software engineering principles and clean coding practices
Experience working within the Software Development Life Cycle (SDLC)
Proficient with Git and version control
Experience with CI/CD pipelines and modern Dev Ops practices
Ability to solve problems through code, not just SQL
Hands-on experience with modern data engineering technologies, including:
Spark / Py Spark
Databricks
Kafka
Flink
Data Lake architectures
Experience building both batch and streaming data solutions
Practical experience delivering solutions on at least one major cloud platform:
Amazon Web Services (AWS)
Relevant services may include:
Data processing and analytics tools (Big Query, Synapse, Redshift, Databricks, Dataflow, Glue, EMR, Dataproc)
Event streaming platforms (Pub/Sub, Event Hubs, Kinesis)
Container platforms (GKE, AKS, EKS)
Dev Ops & Deployment Experience with:
Docker
Infrastructure as Code (Terraform or similar)
Monitoring and observability tools
Experience should be gained through real production projects, not certifications alone.
Additional Valuable Skills
Data modelling (relational and dimensional)
Data warehousing concepts
Modern orchestration and data platforms such as Databricks, Kafka and Microsoft Fabric
Ideal Candidate We're looking for a modern data engineer with a software engineering mindset. Candidates should have proven experience delivering cloud-based data solutions using Python, Spark, CI/CD, Git and modern data platforms. Experience limited to traditional SQL, SSIS or ETL development without modern engineering practices is unlikely to meet current client requirements.
What We Value
Continuous learning and professional development
Ability to contribute immediately within a modern engineering environment
Client-facing consulting experience is advantageous