Seeking a hands-on Data Engineering Lead to spearhead the design, implementation, and delivery of extensive cloud-based data engineering solutions. This role demands profound expertise in data lakes, cloud platforms, ETL/ELT pipelines, and contemporary data architecture. The lead will also manage technical teams and client engagements, overseeing the entire lifecycle from pre-sales to project completion.

Spearhead the design and deployment of scalable, cloud-native data engineering solutions and data lakes. Drive pre-sales initiatives, including client consultations, solution architecture, technical proposal development, and Proof of Concepts (PoCs).

Architect secure and scalable data storage solutions utilizing AWS, Azure, or GCP. Develop and refine ETL/ELT pipelines with tools like Apache Spark, Databricks, AWS Glue, or Azure Data Factory. Implement efficient data transformation processes using serverless technologies such as AWS Lambda or Azure Functions.

Establish robust data governance, security, and data quality frameworks. Foster collaboration with data scientists, BI teams, and business stakeholders to ensure reliable analytics. Lead and mentor a team of data engineers, guiding them through project lifecycles. Optimize cloud infrastructure, storage, and processing costs while upholding performance and reliability standards. Ensure successful project outcomes that align with client and business objectives.

A minimum of 5 years of practical experience in Data Engineering and cloud-based data solutions is essential. Proven expertise in AWS, Azure, or Google Cloud Platform is required. Experience in designing and implementing Data Lakes is a must.

Hands-on proficiency with Apache Spark, Databricks, AWS Glue, Azure Data Factory, or comparable ETL tools is necessary. Familiarity 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. Experience with Docker and Kubernetes is required.

Excellent programming skills in SQL, Python, Scala, or Java are a prerequisite. Experience with cloud data warehouses such as Amazon Redshift, Google BigQuery, or Azure Synapse is needed. Demonstrated leadership capabilities in managing technical teams and delivering projects from pre-sales through implementation are crucial. Outstanding communication and stakeholder management skills are a key requirement.