Seeking a hands-on Data Engineering Lead to spearhead the design, development, and deployment of extensive cloud-based data engineering solutions. This pivotal role requires profound expertise in data lakes, cloud platforms, ETL/ELT pipelines, and contemporary data architectures.

Success in this position involves expertly managing technical teams and client engagements, from initial pre-sales discussions through to successful project completion. The ideal candidate will drive innovation and ensure the delivery of robust data solutions.

Lead the architecture and implementation of scalable, cloud-native data engineering solutions and data lakes.

Drive pre-sales initiatives, including client consultations, solution design, proposal creation, and Proof of Concepts (PoCs).

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

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

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

Establish and maintain strong data governance, security, and data quality frameworks.

Foster collaboration with data scientists, BI teams, and business stakeholders to support reliable analytics.

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

Continuously optimize cloud infrastructure, storage, and processing costs while ensuring high performance and reliability.

Guarantee the successful delivery of projects in alignment with client expectations and business objectives.

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

Demonstrate deep expertise in AWS, Azure, or Google Cloud Platform, coupled with a proven track record in designing and implementing Data Lakes.

Possess hands-on experience with core 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 required.

A strong grasp of data governance, data quality, security, and compliance principles is necessary.

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

Proficiency in programming languages including SQL, Python, Scala, or Java is a must.

Familiarity with cloud data warehouses such as Amazon Redshift, Google BigQuery, or Azure Synapse is advantageous.

Exhibit proven leadership capabilities in managing technical teams and overseeing project delivery from pre-sales to implementation.

Excellent communication and stakeholder management skills are critical for success in this role.