We are seeking an experienced Data Engineer to join our growing data team and help design, build and maintain scalable data infrastructure and pipelines. The successful candidate will work across the modern data stack to transform raw data into reliable, analysable datasets that power analytics and automation.

This is a collaborative role, requiring strong technical skills, attention to detail and a practical mindset focused on reliability and performance.

Key Responsibilities

• Design, build and maintain ETL/ELT pipelines to ingest and transform structured and semi-structured data (APIs, JSON, CSV, HTML, etc.)
• Develop and maintain data models suitable for analytics and downstream consumers
• Work with orchestration tools to schedule, monitor and recover data jobs
• Implement reliable logging, monitoring and alerting for data pipelines and workflows
• Optimise database schemas, queries and indexing to ensure high performance at scale
• Collaborate with engineering, analytics and product teams to define data contracts and requirements
• Ensure data quality through validation, testing and anomaly detection
• Containerise and deploy data components where appropriate and support cloud-based deployments
• Document data lineage, processes and operational runbooks

Required Skills and Experience

• Strong experience with Python for data engineering and automation
• Proven experience designing and building ETL/ELT pipelines
• Solid SQL skills and hands-on experience with relational databases such as PostgreSQL and analytical engines such as DuckDB
• Experience with modern data lake/lakehouse concepts (DuckLake or similar) and data transformation frameworks
• Familiarity with browser automation or web scraping techniques (Playwright, Selenium) for data extraction is a plus
• Knowledge of RESTful API integrations and handling semi-structured payloads (JSON, HTML)
• Experience with asynchronous programming patterns in Python and working with large volumes of data
• Experience with Git and collaborative version control workflows
• Competence with containerisation (Docker) and cloud environments

Preferred Qualifications

• Experience with SQLMesh or other data transformation frameworks
• Experience with orchestration tools for scheduling and monitoring (Airflow, Dagster or similar)
• Familiarity with data warehousing concepts and columnar storage formats
• Exposure to CI/CD and automated testing for data pipelines
• Experience designing for observability and operational runbooks for production systems

We are seeking an experienced Data Engineer to join our growing data team and help design, build and maintain scalable data infrastructure and pipelines. The successful candidate will work across the modern data stack to transform raw data into reliable, analysable datasets that power analytics and automation.

This is a collaborative role, requiring strong technical skills, attention to detail and a practical mindset focused on reliability and performance.

Key Responsibilities

• Design, build and maintain ETL/ELT pipelines to ingest and transform structured and semi-structured data (APIs, JSON, CSV, HTML, etc.)
• Develop and maintain data models suitable for analytics and downstream consumers
• Work with orchestration tools to schedule, monitor and recover data jobs
• Implement reliable logging, monitoring and alerting for data pipelines and workflows
• Optimise database schemas, queries and indexing to ensure high performance at scale
• Collaborate with engineering, analytics and product teams to define data contracts and requirements
• Ensure data quality through validation, testing and anomaly detection
• Containerise and deploy data components where appropriate and support cloud-based deployments
• Document data lineage, processes and operational runbooks

Required Skills and Experience

• Strong experience with Python for data engineering and automation
• Proven experience designing and building ETL/ELT pipelines
• Solid SQL skills and hands-on experience with relational databases such as PostgreSQL and analytical engines such as DuckDB
• Experience with modern data lake/lakehouse concepts (DuckLake or similar) and data transformation frameworks
• Familiarity with browser automation or web scraping techniques (Playwright, Selenium) for data extraction is a plus
• Knowledge of RESTful API integrations and handling semi-structured payloads (JSON, HTML)
• Experience with asynchronous programming patterns in Python and working with large volumes of data
• Experience with Git and collaborative version control workflows
• Competence with containerisation (Docker) and cloud environments

Preferred Qualifications

• Experience with SQLMesh or other data transformation frameworks
• Experience with orchestration tools for scheduling and monitoring (Airflow, Dagster or similar)
• Familiarity with data warehousing concepts and columnar storage formats
• Exposure to CI/CD and automated testing for data pipelines
• Experience designing for observability and operational runbooks for production systems