We are seeking a hands-on Data Engineering Lead to spearhead the design, implementation, and delivery of extensive cloud-based data engineering solutions. The ideal candidate will possess deep expertise in data lakes, cloud platforms, ETL/ELT pipelines, and modern data architecture. This role also involves managing technical teams and client interactions from initial pre-sales discussions through to project completion.

This is a key leadership position focused on building and optimizing robust data infrastructure to support advanced analytics and business objectives.

Lead the end-to-end design and implementation of scalable, cloud-native data engineering solutions and sophisticated data lakes. Drive crucial pre-sales activities, including engaging client discussions, crafting innovative solution designs, preparing technical proposals, and executing Proofs of Concept (PoCs). Architect secure and scalable data storage solutions utilizing AWS, Azure, or GCP. Develop and refine ETL/ELT pipelines with tools such as Apache Spark, Databricks, AWS Glue, or Azure Data Factory. Build efficient data transformation and processing workflows using serverless technologies like AWS Lambda, Azure Functions, or Google Cloud Functions. Establish and maintain comprehensive data governance, security protocols, and data quality frameworks. Collaborate closely with data scientists, BI teams, and business stakeholders to ensure reliable and actionable analytics. Mentor and guide a team of data engineers throughout all phases of the project lifecycle. Continuously optimize cloud infrastructure, storage, and processing costs without compromising performance or reliability. Ensure the successful delivery of projects that precisely align with client expectations and overarching business goals.

Requires a minimum of 5 years of practical experience in Data Engineering and building cloud-based data solutions. Demonstrate strong proficiency in at least one major cloud platform: AWS, Azure, or Google Cloud Platform. Possess hands-on experience in designing and implementing robust Data Lakes. Significant experience with ETL tools such as Apache Spark, Databricks, AWS Glue, or Azure Data Factory is essential. Familiarity with serverless computing environments like AWS Lambda, Azure Functions, or Google Cloud Functions. Exhibit a solid understanding of data governance, data quality, security principles, and compliance regulations. Prior experience with containerization technologies like Docker and orchestration with Kubernetes is expected. Strong programming capabilities in SQL, Python, Scala, or Java are required. Experience working with cloud data warehouses including Amazon Redshift, Google BigQuery, or Azure Synapse. Proven leadership experience in managing technical teams and successfully delivering projects from the pre-sales phase through to final implementation. Excellent communication skills and a demonstrated ability to manage diverse stakeholder relationships.