Technical Project Manager - Data Analytics

Overview

Key Responsibilities

• Coordinate with internal resources, clients and third parties/vendors for execution of  projects.
• Ensure that all projects are delivered on-time, within scope and within budget.
• Assist in the definition of project scope and objectives, involving all relevant  stakeholders and ensuring technical feasibility.
• Develop a detailed project plan to monitor and track progress.
• Manage changes to the project scope, project schedule and project costs using  appropriate verification techniques.
• Measure project performance using appropriate tools and techniques.
• Report and escalate to management as needed.
• Manage the relationship with the client and all stakeholders.
• Perform risk management to minimize project risks.
• Create and maintain comprehensive project documentation.
• Meet with clients to clarify specific requirements of each project.
• Delegate project tasks based on junior staff members' individual strengths, skill sets  and experience levels.
• Track project performance, specifically to analyze the successful completion of short  and long-term goals.
• Control and mitigate project risks by performing the qualitative and quantitative analysis  of risks and trigger events, planning risk response, establishing contingency plans,anticipating dependencies affected by ongoing project changes, and recommending  ways to mitigate future risks to management.
• Work on long-term international projects using emerging technologies and platforms.
• Building complex public health data systems and provide adequate software solutioning  following best software development practices.
• Influence and support the utilization of core technologies and out-of-box solutions.
• Be instrumental in defining the application architecture and deployment architecture.
• Be instrumental in automation of software development and unit testing processes.
• Be instrumental in adoption of new technologies and best of the breed solutions.
• Able to review code and artifacts for quality and signoff to move forward and run extensive peer reviews with special attention to clean code and good tests.
• Contribute in research and implementing POC’s as required over cutting edge technologies.
• Define, Drive and Achieve product / framework roadmaps.

Skills and Requirements

• 5+ years as technical project management.
• Good written and verbal English language communication skills, with an ability to work  effectively in an international environment.
• Background in Public Health / Healthcare industry is preferred.
• Excellent client-facing and internal communication skills.
• Excellent written and verbal communication skills.
• Solid organizational skills, including attention to detail and multitasking abilities.
• Project Management Professional (PMP) certification or in the process of gaining certification within the next six months.
• Master’s Degree in an appropriate field of study or equivalent work experience.
• Expertise in designing and implementing relational and non-relational databases.
• Hands-on experience in analytics projects, including ETL pipelines, data integration, and  processing large datasets.
• Skilled in BI tools, dashboards, and reporting for actionable insights.
• Proficient in Python and SQL for data manipulation, analysis, and automation.
• Experience managing end-to-end delivery of analytics and data science projects.
• Proven stability – demonstrated consistent performance and long-term contributions to  projects.
• Successfully led the end-to-end development and delivery of 2–3 greenfield projects.
• ETL design, data integration, and large-scale dataset processing.
• Relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases.

Good to have

• BI and visualization tools (e.g., Power BI, Tableau, or Superset).
• Familiarity with data standards like HL7, FHIR, or DHIS2 is a strong plus.
• Collaborate with engineers and data scientists to ensure robust data quality,  governance, and analytics readiness.
• Evaluate and integrate emerging tools and technologies in analytics, AI, and data  visualization.
• Lead or contribute to proofs of concept (POCs) for innovative data-driven applications.
• Drive technical documentation, ensuring traceability and reproducibility of analytics  systems.