Technical Manager/ Data Project Manager

  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.

• 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.