Senior Data Engineer (SQL, Fabric, ADF)

As a Senior Data Engineer at TEKenable, you'll design, develop, and maintain data pipelines that are crucial for our clients' data needs. You'll collaborate with data analysts to gather requirements and implement effective data integration solutions. Additionally, ensuring data quality and integrity through rigorous testing and validation will be your priority, alongside optimizing data systems for performance and scalability.

Candidates applying for the Senior Data Engineer role at TEKenable should have experience with the Azure Intelligent Data Platform, including Data Factory, Azure SQL, and Power BI. Proficiency in SQL and Python is essential, along with a solid understanding of data integration and ETL processes. Familiarity with big data technologies like Hadoop or Spark, and working with various data formats such as JSON and CSV is also advantageous.

To qualify for the Senior Data Engineer position at TEKenable, candidates should possess a Bachelor's degree in Computer Science, Engineering, or a related field, along with proven experience in a similar role. A strong foundation in SQL and experience with relational databases are crucial, as is proficiency in programming languages like Python.

Yes! TEKenable follows a 'Remote First' working policy, which means you can work from anywhere while contributing to our exciting projects. We believe that flexibility is vital for both productivity and work-life balance, and we aim to support our employees in achieving that.

As a Senior Data Engineer at TEKenable, you can expect an array of benefits, including income protection, a pension plan, health insurance, and a training budget. We also foster a vibrant work culture with various social events, making it a great place to grow your career while enjoying life outside of work.

When discussing your experience with data pipeline design and implementation, focus on specific projects that showcase your ability to create scalable and efficient pipelines. Highlight your use of technologies such as Azure Data Factory and your approach to ensuring data quality throughout the pipeline.

Emphasize the importance of data quality in your work. Discuss the tools and processes you employ for rigorous testing and validation, as well as your strategies for addressing any data issues that arise during your projects.

Share specific examples of your SQL experience, including the types of queries you’ve written and the databases you’ve worked with. This is an opportunity to demonstrate your analytical skills and your ability to handle large datasets effectively.

When answering, provide a concise overview of a specific problem, your steps in devising a solution, and the outcome. Highlight your technical skills and collaborative approach in working with team members to resolve the issue.

Detail your experience working with Azure services, focusing on the Intelligent Data Platform, including Data Lakes, Data Factory, and Azure Synapse. Mention specific projects where you utilized these services effectively.

Discuss a particular ETL process you've implemented, outlining each step you took from extracting data from various sources to transforming and loading it into the target system. Highlight any challenges faced and how you overcame them.

Talk about the resources you use to keep yourself informed such as online courses, industry articles, tech communities, and conferences. Share how you apply new knowledge to enhance your skills and improve your work.

Identify the programming languages you use regularly, emphasizing SQL and Python. Offer examples of how you have employed these languages in previous projects to solve data challenges or automate processes.

Explain the strategies you implement to optimize data systems for performance, such as indexing, partitioning, or caching. Provide examples of previous work that illustrates your ability to improve efficiency in data processing.

Highlight your communication skills and collaborative approach. Discuss how you work with data analysts to understand their needs, gather requirements, and ensure that the data solutions you deliver are aligned with their expectations.