Role Overview :

We are looking for an experienced Technical Project Manager / Technical Architect with strong expertise in Databricks, Azure Data Services, and data engineering frameworks. The ideal candidate will lead end-to-end data platform initiatives from architecture design and implementation to project delivery and stakeholder management.

This role requires a blend of technical leadership, architectural depth, and hands-on experience in managing modern data transformation projects.

Key Responsibilities :

- Lead and manage the design, development, and delivery of scalable data platforms using Databricks and Azure ecosystems.

- Enable end-to-end data solutions, including ingestion, transformation, storage, and analytics across the Lakehouse architecture.

- Collaborate with business stakeholders, data engineers, and BI teams to translate requirements into robust technical solutions.

- Oversee and guide development teams in building ETL/ELT pipelines using PySpark, SQL, and Delta Lake frameworks.

- Ensure data quality, governance, and compliance with enterprise and regulatory standards (SOC2, GDPR, ISO, etc.).

- Define and implement best practices for CI/CD, version control, and deployment in data engineering workflows.

- Manage multiple projects in parallel, ensuring timely delivery, risk mitigation, and stakeholder alignment.

- Provide technical mentorship to team members, fostering continuous improvement and innovation.

- Coordinate with security architects and BI engineers to ensure secure, validated, and high-performing data solutions.

Project Management :

- Oversee project planning, execution, and delivery, ensuring alignment with timelines, budgets, and business objectives.

- Create and maintain project plans, status reports, and risk registers for ongoing initiatives.

- Coordinate with cross-functional stakeholders to define requirements, scope, and resource allocations.

- Lead Agile / Scrum ceremonies, manage backlogs, and ensure sprint deliverables are achieved.

- Monitor project KPIs and SLAs, proactively addressing risks, blockers, and dependencies.

- Ensure effective communication and coordination between business, technical, and QA teams.

Software Quality Assurance (SQA) :

- Establish and oversee quality assurance frameworks for data engineering and analytics projects.

- Define testing strategies for data pipelines, transformations, and BI outputs, ensuring accuracy and completeness

- Implement automated testing, validation scripts, and reconciliation checks between legacy and new systems.

- Collaborate with QA engineers to validate data accuracy, schema compliance, and performance benchmarks.

- Ensure documentation, version control, and release management adhere to enterprise standards.

Required Skills & Experience :

- 10+ years of experience in data engineering, architecture, or project management roles.

- Proven experience in leading data management projects.

- Understanding of data modeling, data pipelines, and data integration frameworks.

- Experience managing end-to-end project delivery, including planning, execution, and stakeholder communication.

- Solid grasp of DevOps practices for data CI/CD pipelines, Git, and automated deployments.

- Familiarity with Power BI / Qlik / other BI tools and data validation frameworks.

- Excellent communication, leadership, and analytical problem-solving skills.

Preferred Skills :

- Experience with cloud cost optimization and Databricks workspace management.

- Knowledge of Agile / Scrum methodologies and project tracking tools (e.g., JIRA, Azure DevOps).

Good to have :

- knowledge on how data platforms are optimized and scaled

- Exposure to machine learning pipelines and real-time data streaming.

- Certifications in Azure Data Engineer, Databricks Certified Data Engineer / Architect, or equivalent.