Data Engineer - III

The Data Engineer III will design, develop, and maintain scalable, secure, and efficient data pipelines in support of enterprise data product initiatives.

This role involves working with modern cloud-based data technologies, building reliable data processing solutions, and supporting a unified data platform.

The engineer will collaborate across cross-functional teams to collect, parse, manage, analyze, and visualize large datasets while ensuring consistency, data quality, and operational excellence.

Key Responsibilities

• Design, develop, and maintain robust data pipelines to ingest, transform, catalog, and deliver high-quality data into the Common Data Platform.
• Participate in Agile ceremonies and follow established Scaled Agile (SAFe) processes.
• Deliver high-quality data products and services aligned with program standards and best practices.
• Identify, troubleshoot, and resolve issues with data pipelines and analytical data environments.
• Implement monitoring, alerting, and automated remediation for data pipelines and data stores.
• Apply a security-first approach and follow testing, automation, and data engineering best practices.
• Collaborate with product teams, data scientists, analysts, and business partners to understand data needs and deliver the necessary infrastructure and tooling.
• Stay informed on emerging technologies and recommend improvements to enhance data engineering processes and efficiencies.

• Bachelor’s degree in computer science, information systems, or a related field, or equivalent experience.
• Skills across Databricks (PySpark), SQL (Starburst is a plus), Git Lab, CI/CD pipelines, Python, and Tableau.
• Two or more years of experience with tools such as Databricks, Collibra, and Starburst.
• Three or more years of experience with Python and PySpark.
• Experience coding and unit testing in Jupyter notebooks.
• Recent experience working with relational and No
  
  SQL data stores, including STAR and dimensional modeling techniques.
• Two or more years of experience with modern data stack components such as S3, Spark, Airflow, lakehouse architectures, and cloud data warehouses such as Redshift or Snowflake.
• Broad data engineering experience across traditional ETL and big data technologies.
• Experience implementing data engineering solutions in AWS environments.
• Experience building end-to-end pipelines for unstructured and semi-structured data using Spark.

• Experience with real-time data processing solutions.
• Experience contributing to large-scale enterprise data platform initiatives.
• Familiarity with data governance, metadata management, and data quality principles.
• Experience working in Agile or SAFe environments.
• Strong analytical, problem-solving, and communication skills.