What You'll Do:

● Design, build, and maintain scalable data pipelines to ingest, transform, and load data from carriers, clients, and internal systems.

● Develop and maintain data models, data warehouse, and data lake structures that support reporting, analytics, and AI initiatives.

● Write clean, efficient, and well-documented SQL and Python code.

● Implement data quality checks, validation, and monitoring to ensure data accuracy and reliability.

● Optimize pipeline and query performance for large, high-volume datasets.

● Participate in code reviews and contribute to team best practices and data engineering standards.

● Collaborate with product managers, internal customers, analysts, data scientists, and software engineers to define and deliver data solutions.

● Troubleshoot, debug, and resolve pipeline failures and data issues, working with Data Operations on root cause and prevention.

● Support data security, access control, and governance practices.

● Stay current with emerging data technologies and industry trends, including AI tools that improve engineering productivity.

Required

● Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field (or equivalent experience).

● Advanced SQL experience, including complex joins, CTEs, window functions, and performance tuning.

● Databases: MSSQL and PostgreSQL

● Languages: Python (pandas)

● Experience designing and building ETL/ELT pipelines for high-volume data.

● Experience with workflow orchestration and integration tools (e.g., Airflow)

● Understanding of data modeling concepts (dimensional modeling, normalization, data warehouse and lakehouse design) and performance/optimization implications

● Knowledge of containerization tools (e.g., Docker, Kubernetes)

● Experience with cloud data platforms (Azure preferred; e.g., Azure Data Lake, Synapse).

● Familiarity with CI/CD pipelines, Git, and DevOps practices.

Preferred

● Pyspark knowledge is a plus

● Experience with Azure ML Studio,

● Experience with freight, parcel, invoice, or other transactional data is a plus.