Technical Manager/ Data Project Manager

Description

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.