Whitehall Resources
Data Engineer (Databricks)
Cambridge
Posted about 18 hours ago
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Data Engineer (Databricks)
Whitehall Resources currently require an experienced Data Engineer (Databricks) to work with a key client.
Please note this role requires falls INSIDE IR35
Responsibilities
- Design, build, test, deploy and maintain lightweight analytics application, primarily using python and streamlit, to support training and onboarding
- Translate business and user requirements into intuitive application workflows, well-structured data models and transparent metrics.
- Work closely with Data engineers to ensure data pipelines and reusable data sets meet application and analytical needs
- Maintain application code in version control and contribute to testing, technical documentation and development best practices
- Develop and maintain reusable datasets, semantic layer, data quality checks, monitoring and reporting for the data used by the applications.
- Gather user feedback and iteratively improve application usability, accessibility and adoption
- Build dashboards and visualisations where they are the most appropriate way to communicate onboarding, adoption and business metrics
- Support onboarding of new users to the data platform, improving accessibility and usability
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Required Skills and Experience
- Hands on experience developing data or analytic applications in Python using Streamlit, Dash or a similar framework
- Experience designing, testing and maintaining user-facing applications, including the use of version control
- Strong SQL skills and experience analysing and integrating data sets from multiple sources
- Experience with Databricks (or similar modern data platforms)
- Working experience with notebooks and Python data libraries and processing frameworks (e.g. Pandas, PySpark)
- Understanding of deployment, CI/CD, orchestration and application support practices.
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“Nice To Have” Skills and Experience
- Experience building dashboards and visualisation using (Databricks SQL, Power BI, Tableau, or a similar tool)
- Familiarity with data quality frameworks and automated testing approaches
- Understanding of data engineering concepts (pipelines, orchestration, version control)
- Exposure to working in a product-oriented or data platform environment
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Skills
Python
Streamlit
SQL
Databricks
PySpark
Pandas
CI/CD
Version Control
Data Modeling
Data Pipelines
Dash
Power BI
Tableau
Orchestration
Data Quality Frameworks