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.

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.