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
• Bachelors or Masters degree in Computer Science, Informatics, Data Science, Engineering, or a related field.
• AWS Certifications are highly valued.
• Databricks Certifications are considered a strong advantage.

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