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

Requirements

Must-haves

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.
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
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

Nice-to-haves

Experience with Spotfire, Oracle, Informatica, R or data virtualization preferred
Databricks certification preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonsqlci/cd

Platform: workable
Effort: medium