Responsibilities

• Increase the velocity of our transition to a modern data stack (dbt, Airflow, Python, Spark, Synapse, Kubernetes, Docker, + ….).
• Design and collaborate on efficient data ingestion to our data warehouse from data sources including published events, data lakes, data bases and API integrations.
• Develop automation frameworks for data pipelines and analytics processes.
• Set high standards of work including security, infra as code, documentation etc.
• Integrate and maintain the infrastructure used in data analytics workstream (data lakes, data warehouses, automation frameworks).
• Contribute to the design and implementation of a clear, concise data model.
• Contribute to efficient storage of our data in the data warehouse, identifying performance improvements from table and query redesign.
• Write quality ELT code with an eye towards performance and maintainability and empower other analysts to use and contribute to ELT frameworks and tools.
• Improve the overall Data team’s workflow through knowledge sharing, proper documentation, and code review.
• Abstract logic into libraries and patterns of work that enable teams to build value from our data independently.

Qualifications

• You have a bachelors’ degree in a relevant field.
• A minimum of 5 years professional experience in a similar field.
• You enjoy abstracting, generalizing, and creating efficient, scalable solutions where they are needed.
• You enjoy creating patterns and processes as well as solving presented problems.
• Strong foundation of software development best practices (collaborative development via git, testing, continuous integration, deployment pipelines and infrastructure as code).
• Strong SQL Skills.
• Have experience with python.
• Have experience deploying code to production via automated deployments on the cloud.
• Have experience working on associated platforms (Azure, AWS etc).
• Experience building ingestion and/or reporting from streaming datasets and event architectures.
• Experience with distributed compute tools such as Spark and DataBricks.
• Experience with dbt or a good basis to learn it from.
• Experience with orchestration tools such as Airflow.
• Familiarity with using analytics or working with analytics teams.
• Experience with Kubernetes not expected but a plus.
• Experience with data visualization tools such as PowerBI, Looker or Tableau.