- Help design, build and continuously improve the clients online platform.

- Research, suggest and implement new technology solutions following best practices/standards.

- Take responsibility for the resiliency and availability of different products.

- Be a productive member of the team.

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 & Contemporary 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.

Certifications

- AWS Certifications are highly valued.

- Databricks Certifications are considered a strong advantage.

Benefits

- A challenging, innovating environment.

- Opportunities for learning where needed.

📌 Lead Data Engineer - AWS, Databricks & Graph Technologies (Bengaluru)
🏢 GeekSoft Consulting
📍 Bengaluru