Mc Lean VA | DataOps Project Manager | 10+ yrs exp
Rajesh C2C Requirements
Please share suitable profiles from VA, DC, MD States only
Looking for DataOps Project Manager, background exp. should be data analyst, Data modeler, ETL, Informatica.
Title : DataOps Project Manager – Overall experience is 10+yrs
Location : Mclean, VA (onsite all 5 days)
Job Description:
Profile Summary
Results-driven DataOps Project Manager with experience leading cross-functional teams to deliver scalable data solutions. I am skilled in managing data pipelines, cloud data platforms, and analytics initiatives using agile and DevOps methodologies. Adept at bridging the gap between business stakeholders and technical teams to ensure high-quality, reliable, and efficient data operations.
Experienced in overseeing data engineering workflows, ETL processes, data governance, and automation frameworks to improve data availability and decision-making. Proven ability to manage complex projects, optimize data delivery cycles, and ensure alignment with organizational goals.
Core Competencies
Project & Program Management
- Production Environment Management
- Project transition from Development team to DevOPS support team
- Backlog management
- Stakeholder management
DataOps & Data Engineering
- Data pipeline orchestration
- Data quality monitoring
- Data governance and compliance
- CI/CD for data pipelines
Tools & Platforms
- Cloud : AWS, Azure, Google Cloud
- Knowledge of Data Platforms : Snowflake, BigQuery, Redshift
- Knowledge of Orchestration : Airflow, Prefect
- Data Integration : Informatica
- BI : Tableau, Power BI
- DevOps : Git, Jenkins, Docker
- Project tools : Jira, Confluence
Key Responsibilities
- Manage end-to-end data platform and analytics projects.
- Develop Transition plan, KT plan for new project.
- Prepare project plan, Risk assessment, Build SLA, SLO, KPI, etc.
- Drive SRE, and monitoring solution for Data infrastructure to team to stream line datapipelien.
- Coordinate work between data engineers, analysts, and DevOps teams.
- Implement DataOps practices to improve pipeline reliability and deployment speed.
- Monitor data quality, performance, and operational metrics.
- Ensure data governance, security, and compliance standards.
- Deliver dashboards, reporting systems, and analytics solutions.
- Optimize data lifecycle and automation workflows.
- Communicate project progress and insights to executives and stakeholders.