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.
(ref:hirist.tech)
