Experience Requirements:

• Demonstrable experience building data pipelines with Spark, Dask, Pandas, or Ray

• Proficiency in programming languages such as Python (preferred), Scala (preferred), and Java (desirable).

• Hands-on experience working with cloud providers like AWS, Azure, or Google Cloud.

• Proven knowledge of big data platforms like EMR, Databricks, or DataProc.

• Expertise in building and managing data platforms, including data lakehouses, lakes, or warehouses

Other Requirements:

• Bonus points for experience in any of the following areas: automated data quality frameworks, CI/CD pipelines, machine learning models, Infrastructure as Code tools or data visualization tools

Responsibilities:

• Design, build, and maintain scalable data pipelines using tools like Spark, Dask, Pandas, or Ray.

• Develop data platforms such as data lakehouses, data lakes, and data warehouses.

• Work with big data platforms like EMR, Databricks, or DataProc to process and manage large datasets.

• Leverage your expertise in cloud environments (AWS, Azure, or Google Cloud) to ensure the scalability and reliability of our systems.

• Support and operate production systems, ensuring data pipelines run smoothly and efficiently. Collaborate with product, engineering, and data science teams to optimize platform performance and functionality