Lead Data Engineer

Design, develop, test, and deploy high-performance, scalable data pipelines using Apache Spark, Java/Scala across Hadoop and cloud storage platforms.

Lead migration efforts of legacy ETL and data warehouse workloads to cloud-native architectures (AWS preferred) and drive adoption of modern lakehouse architectures.

Lead end-to-end development including requirement analysis, architecture design, mentoring junior engineers, and ensuring adherence to engineering and security standards.

10-12 years experience delivering enterprise-scale Data Warehouse, Data Lake, or Data Lakehouse solutions.

Hands-on expertise with Apache Spark, Scala or Java, Hadoop ecosystem, and object storage platforms such as Amazon S3.

Experience with cloud platforms preferably AWS (Amazon S3, EMR, AWS Glue) and migrating workloads from on-premises to cloud environments.

Strong SQL skills and experience with both relational (Oracle, SQL Server) and NoSQL (Cassandra, DynamoDB) databases.

Experienced with large-scale distributed computing environments and enterprise data engineering projects in financial or closely regulated sectors.

Strong technical leader comfortable shaping architecture that runs seamlessly cross on-premises and cloud with focus on scalability, security, and resilience.

Proven capability in mentoring and collaborating in geographically distributed and matrixed teams within Agile delivery frameworks.