AI/ML/GenAI Architect - Practice

The Practice Head of Data Engineering will be responsible for defining and executing the strategic direction of our data engineering practice. This role involves leading a team of data engineers, developing data architectures, optimizing data pipelines, and ensuring data governance and quality. The ideal candidate will have extensive experience in data engineering, a strong track record of leading large-scale data projects, and a strategic mindset to drive data-driven decision-making across the organization. Minimum of 15 years of experience in data engineering or a related field, with a proven track record of leading large-scale data projects. Responsibilities: Develop and implement a strategic vision for the data engineering practice aligned with the company's goals and objectives. Drive the adoption of best practices in data engineering and ensure alignment with industry trends and emerging technologies. Collaborate with senior leadership and other departments to integrate data engineering solutions into broader business strategies. Team Management: Lead, mentor, and grow a high-performing team of data engineers, fostering a culture of innovation, collaboration, and continuous improvement. Oversee the recruitment, development, and performance management of data engineering talent. Data Architecture & Engineering: Design and implement scalable data architectures, ensuring data integration, quality, and security. Oversee the development and optimization of data pipelines, ETL processes, and data storage solutions. Ensure the effective use of data engineering tools, technologies, and frameworks. Project Management: Manage and prioritize multiple data engineering projects, ensuring timely and successful delivery. Coordinate with project managers, data scientists, and other stakeholders to align project goals and deliverables. Governance & Compliance: Establish and enforce data governance policies and practices to ensure data integrity, security, and compliance with relevant regulations. Monitor data quality metrics and implement strategies to address data issues. Innovation & Continuous Improvement: Stay abreast of industry trends, emerging technologies, and best practices in data engineering. Drive continuous improvement initiatives to enhance data engineering processes and methodologies. Extensive experience in designing and implementing data architectures, data pipelines, and ETL processes. Prior experience in a leadership or management role within a data engineering team. Desired Skills: Expertise in data engineering tools and technologies such as SQL, Python, Spark, Hadoop, and cloud platforms (e.g., AWS, Azure, GCP). Strong knowledge of data modeling, data warehousing, and data integration techniques. Experience with big data technologies and frameworks is highly desirable. Leadership & Communication skills Excellent leadership, mentoring, and team-building skills. Strong strategic thinking and problem-solving abilities. Exceptional communication and interpersonal skills, with the ability to interact effectively with senior leadership and other stakeholders.