Lead Data Engineer

The Lead Data Engineer is responsible for guiding a team of data engineers in the design, development, and maintenance of data infrastructure and pipelines. This role demands a combination of strong technical expertise, leadership abilities, and project management skills. You will ensure the team delivers projects on time and within budget, while maintaining high data engineering standards and fostering a collaborative and innovative environment. Your leadership will be crucial in driving the team's success and contributing to the overall data strategy of the organization. Key Outcomes/Objectives: Team Leadership and Management: Lead a team of data engineers, ensuring effective task allocation and performance. Foster a collaborative and supportive team environment. Mentor and develop team members, enhancing their technical skills and career growth. Project Management and Planning: Plan and manage data engineering projects, ensuring timely delivery within budget. Collaborate on defining and tracking key project metrics and deliverables. Manage project risks and issues, ensuring timely resolution. Data Architecture and Pipeline Development: Define and enforce data engineering standards, best practices, and methodologies. Drive the development and implementation of robust and scalable data architectures and pipelines. Align data architecture with business goals. Ensure high-quality data integration and processing. Process Improvement and Innovation: Identify and implement process improvements to enhance team efficiency and effectiveness. Drive the adoption of data engineering best practices across the organization. Drive the adoption of automation in data engineering processes. Core

Responsibilities: Team Leadership and Management: Lead, mentor, and manage a team of data engineers, providing guidance and support. Assign tasks, monitor progress, and ensure timely completion of deliverables. Conduct regular team meetings, providing feedback and fostering a collaborative environment. Participate in performance reviews and contribute to team member growth and development. Project Management and Planning: Develop project plans, including timelines, resource allocation, and risk assessments. Manage project scope, ensuring alignment with project goals and business requirements. Track project progress, identify and mitigate risks, and ensure timely delivery. Report project status to stakeholders and provide regular updates. Data Architecture and Pipeline Development: Define and implement data engineering standards, best practices, and methodologies. Oversee the design and development of data architectures and pipelines. Ensure adherence to data modeling, data quality, and data security standards. Drive the development and implementation of data integration and processing solutions. Ensure effective monitoring and troubleshooting of data pipelines. Implement data modeling best practices. Process Improvement and Innovation: Identify and implement process improvements to enhance data engineering efficiency and effectiveness. Drive the adoption of data engineering best practices throughout the organization. Monitor and report on key data engineering metrics and trends. Conduct root cause analysis and implement corrective actions. Implement comprehensive monitoring and alerting systems. Collaboration and Communication: Collaborate with data scientists, analysts, and other stakeholders to ensure data quality and availability. Communicate project status, risks, and issues to stakeholders. Facilitate effective communication within the team and across departments. Ensure clear and concise documentation of project requirements and technical specifications. Technical Leadership and Mentorship: Provide technical guidance and mentorship to team members. Lead technical discussions and decision-making within the team. Evaluate and recommend new data engineering tools and technologies. Ensure code quality and maintainability through code reviews and best practices. Community of Practice: Contribute to the appropriate Community of Practice (CoP) for your role by leading discussions, sharing practices, offering firsthand experience to the wider community, engaging in knowledge exchange / cross-pollination to further your craft. Create content and and individually contribute to the stated successful outcomes for this CoP

Qualifications: Education/Experience: Bachelor's degree or equivalent qualifications, or substantial industry experience demonstrating comparable expertise. 8+ years of data engineering experience, with experience in leading teams. Proven experience in managing data engineering projects and delivering them on time and within budget. Experience with DevOps practices and tools. Skills:

Technical Skills: Deep understanding of data engineering concepts, methodologies, and best practices. Expertise in data warehousing, data lakes, and data processing systems. Proficiency in SQL and scripting languages (e.g., Python). Expertise in data processing frameworks (e.g., Apache Spark, Hadoop). Experience with cloud platforms (e.g., AWS, Azure, GCP). Strong understanding of data security, compliance, and governance. Strong analytical and problem-solving skills. Excellent communication and interpersonal skills. Strong leadership and team management skills. Soft Skills / Attributes: Strong work ethic and a proactive attitude. Ability to learn and adapt to new technologies and processes. A passion for working with data and building data infrastructure. Strategic thinking and planning skills. Ability to influence and drive technical decisions. Excellent mentoring and coaching skills.