Key Duties/Tasks:
- Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals.
- Collaborate with data engineers, data consumers, and other team members to produce simple, functional, and elegant solutions that balance the data needs across the organization.
- Solve complex data problems to deliver insights that helps the organization achieve its goals.
- Create data products that will be used throughout the organization.
- Advise, consult, mentor and coach other data and analytic professionals on data standards and practices.
- Develop and deliver documentation on data engineering capabilities, standards, and processes; participate in coaching, mentoring, design reviews and code reviews.
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Technical Qualifications:
- 5+ years relevant and progressive data engineering experience.
- Deep Technical knowledge and experience in Databricks, Python, Spark, Scala, AWS architecture and platform including Glue, Lambda pipelines etc.
- Hands-on experience working with data pipelines using a variety of source and target locations (e.g., Databricks, SQL Server, Data Lake, file-based, SQL and No-SQL database).
- Experience in engineering practices such as development, code refactoring, and leveraging design patterns, CI/CD, and building highly scalable data applications and processes.
- Experience developing batch ETL pipelines; real-time pipelines are a must.
- Knowledge of advanced data engineering concepts such as dimensional modeling, ETL, data governance, data warehousing involving structured and unstructured data.
- Thorough knowledge of SQL Server including T-SQL and stored procedures.
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
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