SUMMARY:
As a Senior Data Engineer, you will have the opportunity to build solutions that ingest, transform, store, and distribute our big data to be consumed by data scientists and our products.
Our data engineers use PySpark/Python, Databricks, and other data engineering technologies and visualization tools to deliver data capabilities and services to our scientists, products, and tools.
This team is focused on building & scaling agentic & generative AI to help drive savings & optimizations within manufacturing domain.
QUALIFICATIONS, SKILLS & EXPERIENCE
4+ years proven ability of professional Data Development experience
3+ years proven ability of developing with Databricks
3+ years of experience with PySpark/Spark
3+ years of experience with SQL
3+ years of experience developing with either Python, Java, or Scala
Full understanding of ETL concepts and Data Warehousing concepts
Experience with CI/CD
Experience with version control software
Strong understanding of Agile Principles (Scrum)
Bachelor s Degree (Computer Science, Management Information Systems, Mathematics, Business Analytics, or STEM)
Bonus Points for experience in the following
Experience with Azure
Experience with Databricks Delta Tables, Delta Lake, Delta Live Tables
Proficient with Relational Data Modeling
Experience with Python Library Development
Experience with Structured Streaming (Spark or otherwise)
Experience with Kafka and/or Azure Event Hub
Experience with GitHub SaaS / GitHub Actions
Experience with Snowflake
Exposure to BI Tooling (Tableau, Power BI, Cognos, etc.)
Key Responsibilities
RESPONSIBILITIES: Take ownership of features and drive them to completion through all phases of the entire SDLC. This includes internal and external facing applications as well as process improvement activities:
Participate in the design and development of Databricks and Cloud-based solutions.
Implement automated unit and integration testing.
Collaborate with architecture and lead engineers to ensure consistent development practices.
Provide mentoring to junior engineers.
Participate in retrospective reviews.
Participate in the estimation process for new work and releases.
Collaborate with other engineers to solve and bring new perspectives to complex problems.
Drive improvements in data engineering practices, procedures, and ways of working.
Embrace new technologies and an ever-changing environment.
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