Director, Data Engineering

Required Skills & Experience

10 years of experience in data engineering, data architecture, or related fields within enterprise environments

5+ years of experience of management experience, leading data engineering teams or technical teams

Strong background with cloud data platforms, data warehousing, and large-scale data pipeline development

Strong cloud experience is a must have. Azure experience preferred, with Microsoft Fabric being a big plus

Hands-on experience with ETL/data integration tools such as Azure Data Factory, AWS Glue, Boomi, or similar

Strong SQL and Python skills

Experience with modern data frameworks and technologies including Spark, Airflow, and Kafka Knowledge of data modeling, data integration, data quality, and enterprise data warehousing

Familiarity with AI/ML data enablement and supporting analytics and reporting initiatives

Strong communication skills with the ability to partner effectively with both technical and non-technical stakeholders

Bachelor's degree required, Master's degree preferred

Job Description

Reporting to the VP of Enterprise Data & Analytics, this Director of Data Engineering will lead the data engineering function while helping define and execute the organization's long-term data roadmap. The role is focused on scaling cloud-based data platforms, modernizing data infrastructure, supporting AI initiatives, and ensuring the organization has reliable, accessible data for reporting, analytics, and business decision making. They will manage a team of around 10-11 with one manager, with opportunity to grow dependent on roadmap and needs. Daily responsibilities include:

Lead the Data Engineering function, overseeing enterprise data platforms, pipelines, and cloud-based data architecture

Help define, develop and execute the teams data roadmap and long-term strategy

Collaborate with BI and analytics leaders to improve data quality, reporting capabilities, and enterprise data models

Drive cloud data strategy and modernization efforts across Azure, Microsoft Fabric, enterprise data warehouses, and data pipeline architecture.

Support and scale AI-enabled data initiatives, including AI-assisted ETL, data ingestion, and automation

Work cross-functionally with business and technical stakeholders, translating business needs into data solutions

Some initiatives include:

Supporting integration projects, including HR data, EMR integration, and upcoming financial system updates

Increase AI across department and organization, with goal to use AI end to end for data quality, automation, code reviews, ETL optimization, and data cleanup standardization

Improve visibility with patient data tracking (accurately capturing data post service)