Director of Data Engineering

Overview

Heritage Christian Services is seeking a Director Data Engineering as  part of the senior IT leadership team. The Director of Data Engineering will lead the organization's data engineering team and drive the development of data systems, infrastructure, and pipelines that enable the efficient collection, processing, and storage of large datasets that drive data-driven decision-making across the organization. This individual leads a team of data engineers, architects, and analysts, ensuring that the organization’s data architecture is scalable, secure, and optimized for performance. The Director of Data Engineering works closely with other IT and business units to align data strategies with overall business objectives and ensure data is accessible, reliable, and actionable.

Pay rate for this position:  $140000 / yr - $143000 / yr

 

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above range represents the organization's good faith and reasonable estimate of the range of possible compensation at the time of posting

 

Responsibilities

Leadership & Team Management:

• Lead and mentor a team of data engineers, ensuring alignment with organizational goals and the development of technical expertise within the team.
• Foster a culture of innovation, continuous improvement, and collaboration.
• Provide guidance on best practices for data engineering, architecture, and data management.
• Manage team performance, including goal setting, performance evaluations, and career development.

 

Data Architecture & Infrastructure:

• Design, implement, and optimize scalable data pipelines and data architectures to support data-driven decision-making.
• Oversee the development of data storage solutions (e.g. data lakes, data warehouses) that are efficient, secure, and reliable.
• Ensure data infrastructure is able to handle growing data volumes and diverse data sources (structured, semi-structured, and unstructured).
• Lead the strategy for data ingestion, integration, and transformation to ensure high-quality and accessible data for analytics and reporting.

 

Collaboration & Stakeholder Engagement:

• Work closely with other technical and business teams to understand data requirements and ensure data architecture aligns with organizational needs.
• Partner with Data Science, Analytics, and Business Intelligence teams to enable advanced analytics and machine learning model deployment.
• Collaborate with product, engineering, and IT teams to ensure seamless data flow and effective data management practices.

 

 

Data Governance & Compliance:

• Ensure compliance with data privacy and security regulations (e.g., NIST, HIPAA, PHI) by designing data systems that protect sensitive information.
• Establish and enforce data governance frameworks and best practices for data quality, metadata management, and data lifecycle management.
• Lead efforts to implement data versioning, auditing, and monitoring to ensure the integrity of data pipelines and platforms.

 

Technology Strategy & Innovation:

• Evaluate and recommend emerging technologies and tools that could enhance the organization’s data capabilities.
• Stay current with industry trends and innovations in data engineering, and assess their relevance and impact on the organization.
• Develop a long-term technology roadmap for data architecture and infrastructure, aligning with business goals and growth.

 

Performance & Monitoring:

• Establish and monitor KPIs to assess the performance, scalability, and reliability of data pipelines and systems.
• Continuously optimize data processing workflows for performance, cost-efficiency, and resource utilization.

 

Technical Expertise:

• Data Engineering: Deep knowledge of data engineering concepts, including data modeling, ETL processes, data pipelines, and integration methods.
• Experience with Data Warehousing, Data Modeling, Semantic Model Definition or Star Schema construction, required.
• Hands-on delivery experience of end-to-end cloud data analytics solutions within Azure or AWS, required.
• Demonstrated experience with a variety of relational and NoSQL technologies (e.g., Azure SQL Server, PostgreSQL).
• Experience in a cloud platform (preferably Azure) and its related technical stack (e.g., Azure Data Factory, Synapse).
• Strong technical understanding of data modeling, data mining, master data management, data inte