Job
Description
Here is a combined Job Description (JD) for a Project & Data Manager (or Project Data Manager ) that merges project management responsibilities with technical data engineering expertise. This hybrid role is ideal for organizations needing someone to lead data-focused projects end-to-end, from strategic planning through to technical execution. Job Title: Project Data Manager Location: [Your Location] Department: Data & Analytics / Project Management Office Reports To: Head of Data Engineering / Director of Projects Job Summary: We are looking for an experienced and versatile Project Data Manager to lead data-centric projects, combining robust project management with deep technical data expertise. This hybrid role is responsible for overseeing the successful delivery of data initiatives—from understanding stakeholder needs to designing and deploying scalable data pipelines and analytics-ready infrastructure. You will ensure alignment between business goals, data strategy, and technical execution while maintaining high standards of data quality and operational efficiency. Key Responsibilities: Project Management: Manage the planning, execution, and delivery of data engineering projects, ensuring they are completed on time, within scope, and within budget. Define project scopes, objectives, timelines, resource plans, and risk mitigation strategies in coordination with stakeholders. Serve as the primary point of contact between technical teams, business units, and leadership. Track project milestones, deliverables, and KPIs using appropriate tools (e.g., Jira, Asana, MS Project). Facilitate project meetings, status updates, and stakeholder communications. Data Engineering & Technical Responsibilities: Design, develop, and maintain scalable data pipelines and ETL/ELT processes to ingest, transform, and store data from various structured and unstructured sources. Implement efficient data models and schemas to support business intelligence, reporting, and machine learning use cases. Ensure data accuracy and quality by building validation, monitoring, and error-handling mechanisms. Collaborate closely with analysts, data scientists, and business units to deliver clean, reliable, and accessible data. Optimize performance, scalability, and cost-effectiveness of data infrastructure and cloud-based platforms. Stay up to date with emerging trends in data engineering, cloud computing, and project management best practices. Required Qualifications: Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field. Minimum 8 years of experience in data engineering or related technical roles, with at least 2 years of experience in a project management capacity. Proficient in SQL, Python, and data pipeline tools (e.g., Airflow, Kafka, Talend, Informatica). Hands-on experience with relational and NoSQL databases (e.g., Postgres, MongoDB, Oracle), data warehousing platforms (e.g., Snowflake, Redshift, BigQuery), and distributed computing frameworks (e.g., Spark, Flink). Familiarity with cloud services (AWS, Azure, GCP) and infrastructure-as-code concepts. Understanding of data governance, data security, and compliance best practices. Strong working knowledge of Agile, Scrum, or Waterfall methodologies. Excellent analytical thinking, problem-solving skills, and attention to detail. Effective verbal and written communication skills for cross-functional collaboration and stakeholder management. Preferred Qualifications: PMP, PRINCE2, or Agile/Scrum certification. Experience with data visualization platforms such as Power BI, Tableau, or Looker. Experience building CI/CD pipelines for data deployment and managing version control (e.g., Git). Prior experience in leading data transformation initiatives or enterprise-wide data modernization projects. Would you like this tailored further for a specific industry (e.g., healthcare, fintech, SaaS) or level (mid-senior, director, etc.)? Show more Show less
