Overview
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, archite
Full job description
Full Job Description
- 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
Qualifications And Requirements
Mid-Senior level
Requirements & qualifications
Mid-Senior level
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