At a Glance
- Tasks: Build and optimise data pipelines on Azure Databricks for economic data analysis.
- Company: Join a leading financial services firm in London with a focus on innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on continuous learning and development.
- Why this job: Make an impact in economic data engineering while working with cutting-edge technologies.
- Qualifications: 8+ years in data engineering, strong skills in Python, Spark, and Azure Databricks.
The predicted salary is between 60000 - 80000 £ per year.
Location: London, UK (3 days in the office)
SC Cleared: Required
Job Type: Full-Time
Experience: 8+ years
Job Summary: We are seeking a highly skilled and experienced Senior Data Engineer to join our team and contribute to the development and maintenance of our cutting-edge Azure Databricks platform for economic data. This platform is critical for our Monetary Analysis, Forecasting, and Modelling activities. The Senior Data Engineer will be responsible for building and optimising data pipelines, implementing data transformations, and ensuring data quality and reliability. This role requires a strong understanding of data engineering principles, big data technologies, cloud computing (specifically Azure), and experience working with large datasets.
Key Responsibilities:
- Data Pipeline Development & Optimisation: Design, develop, and maintain robust and scalable data pipelines for ingesting, transforming, and loading data from various sources (e.g., APIs, databases, financial data providers) into the Azure Databricks platform. Optimise data pipelines for performance, efficiency, and cost-effectiveness. Implement data quality checks and validation rules within data pipelines.
- Data Transformation & Processing: Implement complex data transformations using Spark (PySpark or Scala) and other relevant technologies. Develop and maintain data processing logic for cleaning, enriching, and aggregating data. Ensure data consistency and accuracy throughout the data lifecycle.
- Azure Databricks Implementation: Work extensively with Azure Databricks Unity Catalog, including Delta Lake, Spark SQL, and other relevant services. Implement best practices for Databricks development and deployment. Optimise Databricks workloads for performance and cost. Need to program using the languages such as SQL, Python, R, YAML and JavaScript.
- Data Integration: Integrate data from various sources, including relational databases, APIs, and streaming data sources. Implement data integration patterns and best practices. Work with API developers to ensure seamless data exchange.
- Data Quality & Governance: Hands-on experience to use Azure Purview for data quality and data governance. Implement data quality monitoring and alerting processes. Work with data governance teams to ensure compliance with data governance policies and standards. Implement data lineage tracking and metadata management processes.
- Collaboration & Communication: Collaborate closely with data scientists, economists, and other technical teams to understand data requirements and translate them into technical solutions. Communicate technical concepts effectively to both technical and non-technical audiences. Participate in code reviews and knowledge sharing sessions.
- Automation & DevOps: Implement automation for data pipeline deployments and other data engineering tasks. Work with DevOps teams to implement and build CI/CD pipelines for environmental deployments. Promote and implement DevOps best practices.
Essential Skills & Experience:
- 10+ years of experience in data engineering, with at least 3+ years of hands-on experience with Azure Databricks.
- Strong proficiency in Python and Spark (PySpark) or Scala.
- Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns.
- Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage, and Azure SQL Database.
- Experience working with large datasets and complex data pipelines.
- Experience with data architecture design and data pipeline optimization.
- Proven expertise with Databricks, including hands-on implementation experience and certifications.
- Experience with SQL and NoSQL databases.
- Experience with data quality and data governance processes.
- Experience with version control systems (e.g., Git).
- Experience with Agile development methodologies.
- Excellent communication, interpersonal, and problem-solving skills.
- Experience with streaming data technologies (e.g., Kafka, Azure Event Hubs).
- Experience with data visualisation tools (e.g., Tableau, Power BI).
- Experience with DevOps tools and practices (e.g., Azure DevOps, Jenkins, Docker, Kubernetes).
- Experience working in a financial services or economic data environment.
- Azure certifications related to data engineering (e.g., Azure Data Engineer Associate).
Senior Data Engineer employer: Mastek
Join a forward-thinking company in London that values innovation and collaboration, offering a dynamic work culture where your contributions as a Senior Data Engineer will directly impact our cutting-edge Azure Databricks platform. With a strong emphasis on employee growth, we provide ample opportunities for professional development and training, alongside a flexible working arrangement of three days in the office. Experience a supportive environment that prioritises data quality and governance while fostering teamwork with economists and data scientists to drive meaningful economic analysis.
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We think this is how you could land Senior Data Engineer
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We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Mastek. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Mastek
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Mastek!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
