Seeking a seasoned, hands-on Data Engineering Lead to spearhead the design, development, and deployment of extensive cloud-based data engineering solutions. This role demands deep expertise in data lakes, cloud environments, robust ETL/ELT pipelines, and contemporary data architecture. The successful candidate will also manage technical teams and client interactions, from initial sales discussions through to project completion.

This is an opportunity to shape and deliver innovative data solutions within a dynamic engineering environment.

Lead the architecture and implementation of scalable cloud data engineering solutions and data lakes. Drive client engagements, including pre-sales discussions, solution design, proposal creation, and proof-of-concept development. Design secure and scalable data storage architectures on AWS, Azure, or GCP. Develop and optimize ETL/ELT pipelines using technologies like Apache Spark, Databricks, AWS Glue, or Azure Data Factory. Build efficient data transformation workflows with serverless functions. Establish comprehensive data governance, security, and quality frameworks. Foster collaboration with data scientists, BI teams, and business stakeholders for reliable analytics. Mentor and guide a team of data engineers throughout project lifecycles. Optimize cloud infrastructure costs without compromising performance and reliability, ensuring successful project delivery aligned with client and business objectives.

A minimum of 5 years of practical experience in Data Engineering and cloud-based data solutions is essential. Demonstrable expertise in AWS, Azure, or Google Cloud Platform is required. Experience in designing and implementing Data Lakes is a must. Proficiency with Apache Spark, Databricks, AWS Glue, Azure Data Factory, or similar ETL tools is necessary. Experience with serverless computing (AWS Lambda, Azure Functions, Google Cloud Functions) is expected. A strong grasp of data governance, data quality, security, and compliance principles is vital. Familiarity with Docker and Kubernetes is required. Excellent programming skills in SQL, Python, Scala, or Java are needed. Experience with cloud data warehouses such as Amazon Redshift, Google BigQuery, or Azure Synapse is important. Proven leadership capabilities in managing technical teams and delivering projects end-to-end are crucial. Outstanding communication and stakeholder management skills are essential for this role.