We are looking for experienced Data Engineering Managers who are passionate about building scalable data platforms, driving technical excellence, and leading high-performing teams. If you enjoy architecting modern data solutions, mentoring engineers, collaborating with business stakeholders, and leveraging technologies such as Databricks, Snowflake, Spark, and cloud platforms, this role is for you. We value leaders who can balance strategy and execution while fostering innovation, best practices, and a strong engineering culture. Role Overview As a Manager – Databricks & Snowflake Data Engineering, you will lead a team of data engineers responsible for designing, building, and maintaining scalable data platforms and pipelines. You will work closely with business stakeholders, architects, analytics teams, and leadership to deliver reliable, high-quality data solutions that enable data-driven decision-making across the organization. This role combines technical leadership, people management, delivery oversight, and solution architecture. You will drive modernization initiatives, establish engineering best practices, and ensure successful execution of data engineering programs leveraging Databricks, Snowflake, Spark, and cloud technologies. ### Responsibilities - Lead and mentor a team of data engineers, fostering technical growth and engineering excellence. - Drive the design, development, and implementation of scalable data platforms, data pipelines, and data products. - Partner with business stakeholders to understand requirements and translate them into technical solutions. - Define data architecture, engineering standards, and best practices across projects. - Oversee the development of ETL/ELT frameworks using Databricks, Snowflake, Spark, and cloud-native services. - Ensure data quality, governance, security, and compliance standards are implemented across data platforms. - Manage project delivery, resource planning, risk mitigation, and stakeholder communications. - Collaborate with cross-functional teams including Analytics, Data Science, AI/ML, and Enterprise Architecture. - Drive performance tuning, platform optimization, and cost management initiatives. - Evaluate emerging technologies and lead modernization efforts to improve scalability and efficiency. - Support hiring, capability building, and career development initiatives within the team. ### Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field. - 6+ years of experience in Data Engineering, Data Warehousing, or Big Data technologies. - 2+ years of experience leading or managing engineering teams. - Strong expertise in Databricks, Snowflake, Spark, SQL, and Python. - Experience designing and implementing enterprise-scale data solutions and cloud-based data architectures. - Strong understanding of data warehousing, dimensional modeling, ETL/ELT frameworks, and data governance principles. - Experience with Azure, AWS, or GCP cloud platforms. - Proven track record of delivering complex data engineering projects and managing stakeholder expectations. - Excellent leadership, communication, and problem-solving skills. - Experience supporting Data Science, AI, and Analytics workloads is highly desirable.