Design end-to-end data streams, storage, data serving systems and analytical workflows, including overall architecture, capabilities, platforms, tools, and governing processes.
Build data pipelines to extract, transform, and load data from a variety of sources.
Help define/improve best practices, guidelines and integration with other enterprise solutions
Build data warehouses and data lakes to store and manage data.
Develop and deploy data processing and analytics tools.
Work with data scientists to develop and deploy data-driven solutions.
Stay up to date on the latest data technologies.
Provide direction to the Data Engineers and perform code review for them.
Setting up metadata / master data structures to support the transformation pipelines built in Databricks.
Strong fundamental knowledge of data structures and performance considerations.

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Machine Learning, Statistic, Data Science or other related technical disciplines.
8+ years of relevant experience in demonstrating a high degree of proficiency in designing and developing complex, high-quality big-data platforms serving analytical solutions Strong understanding of data engineering principles and practices.
Experience with a variety of data technologies, such as Databricks, ADF (Azure Data Factory)
Extensive experience with data modeling and designing/supporting both streaming and batch ETL pipelines
Proficient in SQL/NoSQL.
Strong programming proficiency using 1 or 2 of the following languages: Python, Scala.
Experience with cloud computing platforms, such as AWS or Azure.
Experience with big data technologies, such as Hadoop, Spark, Kafka, and Hive is a plus.
Experience with BI tools like Tableau or Power BI is a plus.
Ability to learn new technologies quickly.
Ability to work independently and as part of a team.
Excellent written and verbal communication skills.