Lead Data Engineer - AWS, Databricks & Graph Technologies

Requirements

• Minimum 10 years of experience in Data Engineering.
  


• Experienced Lead Data Engineer to design, develop, and implement modern cloud-native data platforms that enable analytics, artificial intelligence, and data-driven decision-making.
  


• Work closely with architects, engineers, and business stakeholders to solve complex data challenges across diverse industries, including government, financial services, infrastructure, energy, and manufacturing.
  


• As a technical leader, play a key role in defining data architecture standards, mentoring Data Engineers, and driving best practices within the Data & Analytics team.
  


• Design and implement scalable Data Lakes, Data Warehouses, and Lakehouse architectures.
  


• Develop and maintain robust ETL/ELT pipelines for both batch and real-time data processing.
  


• Design and optimize data models while ensuring data quality, governance, and consistency.
  


• Build and support cloud-native data solutions on AWS.
  


• Develop event-driven and serverless architectures to support modern data platforms.
  


• Enable analytics, business intelligence, artificial intelligence, and machine learning initiatives through efficient data solutions.
  


• Provide technical leadership, mentor Data Engineers, and contribute to architecture and design decisions.
  


• Collaborate within Agile teams to deliver innovative and scalable client solutions.
  


• Strong experience designing and implementing cloud-native data platforms.
  


• Deep understanding of Data Engineering principles, ETL/ELT processes, Data Modeling, and Data Warehousing concepts.
  


• AWS Cloud Technologies: Amazon S3, Amazon Redshift, AWS Glue, Amazon Athena, AWS Lambda, Amazon DynamoDB, Amazon ECS/Fargate, AWS Step Functions, Amazon SNS/SQS
  


• Data Engineering & Big Data Technologies: Apache Spark, Apache Kafka, SQL, Python
  


• Relational databases such as PostgreSQL, Oracle, SQL Server, or equivalent.
  


• NoSQL databases including MongoDB, DynamoDB, Cassandra, or similar platforms.
  


• Engineering & DevOps: Git version control, Docker, CI/CD pipelines, Infrastructure as Code (IaC), Agile/Scrum methodologies
  Databricks & Modern Data Platforms: Databricks, Delta Lake, Unity Catalog, Lakehouse Architecture
  


• Experience with Snowflake, Microsoft Fabric, or similar modern data platforms
  


• Graph Technologies: Neo4j, Amazon Neptune, GraphDB, or similar graph databases, Graph Data Modeling, Graph Analytics, Knowledge Graphs, Data Lineage and Dependency Mapping
  


• Additional Preferred Skills: Event-Driven Architecture, Microservices Architecture, Machine Learning and Advanced Analytics
  


• Experience with Microsoft Azure Data Platform technologies
  


• Bachelor’s or Master’s degree in Computer Science, Informatics, Data Science, Engineering, or a related field.
  

• AWS Certifications are highly valued.
  


• Databricks Certifications are considered a strong advantage.
  
  
  

Benefits

• A challenging, innovating environment.
  


• Opportunities for learning where needed.