Data Engineering Lead

Role Overview: As a Data Engineering Lead at Ascentt, you will be responsible for overseeing and guiding the organization's data engineering function. You will combine your deep technical expertise in big data and data engineering with strong leadership skills to drive innovation, best practices, and excellence across the data engineering teams. Your role will involve establishing and evolving the strategic direction for the data engineering practice, defining and implementing best practices, leading the design and architecture of scalable data solutions, overseeing multiple data engineering teams and projects, mentoring and developing talent, and collaborating with other practice leads and senior leadership to align initiatives with broader strategies. Key Responsibilities: - Establish and evolve the strategic direction for the data engineering practice across the organization. - Define and implement best practices, standards, and methodologies for data engineering processes and technologies. - Lead the design and architecture of scalable, enterprise-wide data solutions and platforms. - Oversee multiple data engineering teams and projects, ensuring alignment with organizational goals and technical standards. - Mentor and develop data engineering talent, fostering a culture of continuous learning and innovation. - Collaborate with other practice leads and senior leadership to align data engineering initiatives with broader technology and business strategies. - Stay abreast of emerging trends and technologies in data engineering and feature engineering. - Drive the selection, implementation, and optimization of data engineering tools and technologies. - Establish KPIs and metrics to measure the effectiveness and efficiency of data engineering practices. - Represent the data engineering practice in executive-level meetings and decision-making processes. - Guide the adoption of cloud-based data solutions and migration strategies. - Participate in industry events, conferences, and communities to enhance the organization's reputation in data engineering. Qualification Required: - 10+ years of experience in data engineering roles, with at least 3 years in leadership positions. - Deep expertise in big data technologies, cloud platforms, and data integration tools and programming languages such as Scala, PySpark with Spark knowledge. - Strong understanding of data governance, security, and compliance requirements. - Proven track record of successfully leading large-scale data engineering initiatives. - Excellent communication skills with the ability to convey complex technical concepts to both technical and non-technical audiences. - Strong leadership and mentoring abilities with experience managing and developing teams. - Experience in establishing or significantly growing a data engineering practice. - Knowledge of machine learning and AI technologies and their integration with data engineering. - Familiarity with Agile and DevOps methodologies. - Relevant industry certifications (e.g., AWS Certified Big Data, Google Cloud Professional Data Engineer, Databricks, Snowflake). - Experience in a consulting or professional services environment. - Published articles or speaking engagements related to data engineering and/or feature engineering. - Good knowledge on GraphDBs, VectorDBs, and building RAG frameworks.,