The job, in the employer's words
- You’ll build and maintain data pipelines and datasets that support reporting, visualization, machine learning and data-driven decision making across finance, sales, supply chain, marketing and other business functions.
- Your responsibilities will include:
- Developing and maintaining scalable data pipelines and reusable datasets within a Databricks lakehouse environment
- Ingesting, validating, cleaning and transforming structured and unstructured data from multiple sources using Python, PySpark and SQL
- Building data models and reporting datasets that support Tableau and established analytics tools such as Spotfire
- Integrating established data warehouse and reporting systems with modern cloud analytics platforms
- Improving pipeline reliability, data quality and processing performance
- Implementing automated testing, monitoring, documentation and CI/CD practices
- Partnering with analysts, architects, developers and business stakeholders to deliver practical data solutions
- Providing technical guidance, reviewing code and mentoring other data engineer
Requirements
- 7+ years of experience in data engineering, including building and supporting production data pipelines
- Strong SQL skills and a solid understanding of data modeling, database design and data warehousing
- Hands-on experience with Databricks, Apache Spark and cloud data platforms
- Strong proficiency in Python and PySpark
- Experience developing ETL/ELT pipelines and integrating data from multiple source systems
- Experience preparing reliable datasets for Tableau or comparable business intelligence tools
- Familiarity with source control, automated testing and CI/CD practices
- Ability to troubleshoot complex data issues and optimize pipeline performance
- Strong communication skills and experience collaborating with technical teams and business stakeholders
- Experience with Spotfire, Oracle, Informatica, R or data virtualization preferred
- Databricks certification preferred
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