Seeking a hands-on Data Engineering Lead to spearhead the design, implementation, and delivery of extensive cloud-based data engineering solutions. This role requires deep expertise in data lakes, cloud environments, ETL/ELT processes, and contemporary data architecture. The lead will also be responsible for managing technical teams and client engagements, from initial pre-sales discussions through to successful project completion.

This is a key opportunity to shape and execute data strategies within our Engineering as a Service/Operate division, driving innovation and efficiency in data solutions.

Lead the architecture and deployment of robust, scalable cloud data engineering solutions and data lakes.

Drive pre-sales initiatives, including client consultations, solution architecture, technical proposal development, and Proof of Concepts (PoCs).

Design and implement secure, scalable data storage solutions leveraging AWS, Azure, or GCP.

Develop and optimize ETL/ELT pipelines using tools like Apache Spark, Databricks, AWS Glue, or Azure Data Factory.

Build efficient data transformation and processing workflows with serverless technologies such as AWS Lambda or Azure Functions.

Establish comprehensive data governance, security, and data quality frameworks.

Collaborate closely with data scientists, BI specialists, and business stakeholders to ensure reliable analytics capabilities.

Provide leadership and mentorship to data engineering teams throughout the project lifecycle.

Optimize cloud infrastructure, storage, and processing expenses while upholding performance and reliability standards.

Ensure successful project outcomes that align with client needs and business objectives.

Demonstrate at least 5 years of practical experience in Data Engineering and cloud-native data solutions.

Possess strong proficiency in at least one major cloud platform: AWS, Azure, or Google Cloud Platform.

Show proven experience in designing and implementing Data Lakes.

Have hands-on experience with ETL tools such as Apache Spark, Databricks, AWS Glue, or Azure Data Factory.

Familiarity with serverless computing environments like AWS Lambda, Azure Functions, or Google Cloud Functions is essential.

Exhibit a solid understanding of data governance, data quality, security, and compliance principles.

Experience with containerization technologies like Docker and orchestration with Kubernetes is required.

Strong programming skills in SQL, Python, Scala, or Java are necessary.

Experience with cloud data warehouses including Amazon Redshift, Google BigQuery, or Azure Synapse is expected.

Show documented leadership experience in managing technical teams and delivering projects from pre-sales through to implementation.

Possess excellent communication and stakeholder management abilities.