Director Data Engineering

Position Summary

We are seeking a dynamic and visionary Director of Data Engineering Practice GCP to lead our enterprise data engineering function. In this role, the Director of Data Engineering Practice defines the core standards and practices for data engineering. This includes ensuring code stability and scalability, optimizing team efficiency through improved processes, fostering stronger collaboration within and between teams, and developing the Data Engineering community culture. You will play a key leadership role in building a high-performing data engineering team, implementing best practices, and delivering enterprise-grade solutions to support analytics, AI/ML, and business decision-making.

As a Director of Data Engineering, you will provide strategic and operational leadership for a team of data engineers, aligning all engineering initiatives with organizational goals. This includes developing comprehensive data strategies, overseeing data architecture, managing complex projects, and monitoring performance. A deep expertise in Google Cloud Platform technologies is essential. You will be responsible for ensuring the integrity of data pipelines, upholding stringent data governance standards, and maintaining full compliance. Ultimately, your success will be measured by your team's ability to architect and implement unique, high-quality data pipeline solutions for structured, semi-structured, and unstructured data, facilitating efficient data flow throughout a complex and regulated enterprise.

Major Responsibilities:

• Recruit, develop, and manage employees within scope of responsibility.

• Automate and/or improve processes associated with code reviews, developer testing, support frameworks.

• Consult on deep dives into extremely complex issues, their root causes, and their solutions. Ensure timely proactive issue identification, escalation & resolution.

• Establish a strong inclusive culture, encouraging innovation, inclusion, teamwork, and diversity.

• Manage a dynamic team of IT professional including data engineers.

• Proactively evaluate and champion/influence change across all delivery teams that will mitigate risks to quality and provide multiple viable options for mitigation of such risks.

• Promote change control processes and ensure program/project communications.

• Provide consultation to key staff members on business value of proposed changes.

• Provide leadership and mentoring to less experienced team members.

• Practices and adheres to the Code of Conduct philosophy and Mission and Value Statement.

• Develop and implement standardized hiring frameworks to attract and retain top engineering talent.

• Establish training strategies that enable engineers to work across teams and products, promoting cost-efficient resource management.

• Foster career growth through structured feedback mechanisms and mentorship programs, ensuring alignment with organizational needs.

• Champion a strong, innovation-driven engineering culture through knowledge-sharing initiatives.

• Advocate for continuous learning, creating incentives for professional development and technical skill enhancement.

• Identify bottlenecks in development workflows and drive strategic process improvements across engineering teams.

• Collaborate with teams to refine agile execution,ensuring alignment and addressing inefficiencies.

• Promote the use of AI, automation, and scalable architectures in partnership with Data Architecture and Business teams.

• Facilitate technical collaboration between teams managing interconnected data systems, ensuring seamless integration and execution.

• Develop strategies to resolve dependencies across teams,eliminating silos within products and delivery workflows.

• Define a comprehensive technical roadmap that aligns with business objectives and engineering goals.

• Ensure cohesion between engineering pods and the Data Architecture team, integrating vendor and internal teams into the strategic framework.

• Establish and enforce engineering best practices, minimizing technical debt while ensuring timely execution of commitments.

• Define and execute the strategic vision for enterprise data infrastructure and engineering excellence

• Drive innovation in data engineering through the adoption of DevOps, DataOps, and CI/CD automation frameworks

• Design a data engineering roadmap and execute the vision behind it

• Must maintain privacy and confidentiality of patient information

• Manage 3rd-party resources and critical data integration vendors

Education & Experience:

• Bachelors or Masters degree in Computer Science, Engineering, or a related field
• 15+ years of experience in data engineering/software development, including at least 3-5 years in a leadership role.
• Proven experience with modern data architecture paradigms: data lakes, Lakehouse, real-time streaming, and microservices.
• Expert knowledge of Software Development tenants and principles.
• Knowledge of enterprise data with the ability to effectively interpret, communicate and educate others is preferred.
• Excellent written and verbal communication skills with the ability to present complex technical information in a clear and concise manner to a variety of audiences.
• Highly detail oriented and organized with the ability to multi-task and work in a fast-paced environment.
• Must have experience of interpreting technical specifications, detailed requirements and developing test cases.
• Proven experience leading a team of resources both onsite and in remote locations.
• Problem-solving skills: Individuals at our company have well-honed analytical skills coupled with business acumen to structure problems, deliver solutions, and communicate insights.
• Experience with relational database management systems: Teradata and DB2
• Strong understanding of data integration, source target mapping and business rules transformation in a heterogeneous systems environment.

Licenses, Certifications, & Training:

• GCP Professional Data Engineer or Cloud Architect certification.
• Experience in multi-cloud or hybrid cloud data architecture.
• Background in agile delivery and cross-functional product teams.