Job Opportunities API

  Lead and manage large-scale  Data Engineering and Data Modernization projects .Hands-on experience in managing Data projects end-to-end — effort estimation, scoping, project plan, timelines, team allocation, stakeholder management   Drive end-to-end delivery of  Data Lake build and migration initiatives .Hands-on experience with modern Data technologies like PySpark, SQL, CML, Python on any cloud; preferred GCP   Lead  PySpark migration and optimization projects , ensuring performance and scalability. Transform business requirements into Data solutions, manage risks, issues and dependencies   Design and implement modern data architectures, including  Data Lakes, Data Warehouses, and Lakehouse solutions .   Collaborate with business stakeholders, architects, and engineering teams to define data strategies and roadmaps.   Provide technical leadership and mentorship to Data Engineers and Developers.   Ensure best practices around: Data governance Data quality Security and compliance Performance optimization   Lead data platform modernization initiatives across cloud environments.   Review solution designs, architecture documents, and implementation approaches.   Manage project planning, resource allocation, risks, and delivery timelines.   Drive Agile delivery and ensure successful project execution. Requirements Hands-on experience in managing Data projects end-to-end — effort estimation, scoping, project plan, timelines, team allocation, stakeholder management Hands-on experience with modern Data technologies like PySpark, SQL, CML, Python on any cloud; preferred GCP Transform business requirements into Data solutions, manage risks, issues and dependencies Proven experience in delivering: Data Lake implementation projects Data Lake migration programs PySpark migration projects Large-scale data transformation initiatives Technical Skills Strong expertise in: Python PySpark Spark SQL SQL ETL/ELT frameworks Experience with: Hadoop ecosystem Data Lakes and Lakehouse architectures Distributed data processing frameworks Strong understanding of: Data modeling Data integration patterns Batch and real-time processing Experience with cloud platforms such as: AWS Azure GCP Hands-on experience with: Data migration strategies Performance tuning and optimisation CI/CD and DevOps practices for data platforms Preferred Skills Experience with: Databricks Delta Lake Apache Airflow Kafka Snowflake Kubernetes and Docker Experience in Banking, Financial Services, or other large enterprise environments. Exposure to data governance and data quality frameworks.