Seeking a proactive Data Engineering Lead to spearhead the design, development, and deployment of extensive cloud-based data solutions. This role requires deep expertise in data lakes, cloud technologies, ETL/ELT processes, and contemporary data architecture. You will also be responsible for guiding technical teams and managing client relationships from initial contact through project completion.

This position offers an opportunity to shape and implement cutting-edge data engineering strategies, ensuring scalable and secure data infrastructure for complex projects.

Lead the architectural design and implementation of robust, cloud-native data engineering solutions and data lakes.

Drive crucial pre-sales engagements, including client consultations, solution architecture, proposal creation, and proof-of-concept development.

Design secure and scalable data storage architectures utilizing AWS, Azure, or GCP services.

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

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

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

Collaborate closely with data scientists, BI specialists, and business stakeholders to ensure data availability and reliability for analytics.

Provide leadership and mentorship to a team of data engineers throughout the project lifecycle.

Optimize cloud infrastructure, storage, and processing expenditures while upholding performance and stability.

Guarantee successful project outcomes that align with client expectations and business goals.

A minimum of 5 years of practical experience in Data Engineering and cloud-based data solutions is essential.

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

Proven experience in designing and implementing enterprise-level Data Lakes.

Hands-on expertise with distributed data processing frameworks like Apache Spark and tools such as Databricks, AWS Glue, or Azure Data Factory.

Experience in leveraging serverless computing for data processing, including AWS Lambda, Azure Functions, or Google Cloud Functions.

A solid understanding of data governance principles, data quality standards, security best practices, and compliance requirements.

Familiarity with containerization technologies like Docker and orchestration with Kubernetes.

Strong programming skills in SQL and at least one of the following: Python, Scala, or Java.

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

Substantial leadership experience in managing technical teams and delivering projects from the initial sales phase through to successful implementation.

Exceptional communication and stakeholder management abilities are critical for success in this role.