My3Tech

DataOps Engineer/ Data Engineer

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a DataOps Engineer with a contract length of unspecified duration, offering a pay rate of "unknown." It is remote, based in Montpelier, VT. Key skills include Azure Data Engineering, Power BI, and data governance certifications.
🌎 - Country
United States
πŸ’± - Currency
$ USD
-
πŸ’° - Day rate
Unknown
-
πŸ—“οΈ - Date
May 15, 2026
πŸ•’ - Duration
Unknown
-
🏝️ - Location
Remote
-
πŸ“„ - Contract
Unknown
-
πŸ”’ - Security
Unknown
-
πŸ“ - Location detailed
Vermont, United States
-
🧠 - Skills detailed
#Data Engineering #Azure #Data Management #Scrum #Data Analysis #Databricks #Lean #Data Quality #AI (Artificial Intelligence) #Data Governance #BI (Business Intelligence) #Microsoft Power BI #Security #Metadata #DataOps #Documentation
Role description
Hello, Hope you are doing good. Position: DataOps Engineer Location: Montpelier, VT (Remote) Preferred Qualifications: β€’ Microsoft Certified: Azure Data Engineer Associate β€’ Microsoft Certified: Power BI Data Analyst Associate (PL-300) β€’ Databricks Data Engineer Associate (or Professional) β€’ Azure AI Engineer Associate β€’ DAMA CDMP (Certified Data Management Professional) β€’ DCAM (EDM Council Data Management Certification) β€’ IIBA CCBA or CBAP β€’ PMI-PBA β€’ DAMA CDMP or DCAM β€’ Lean Six Sigma β€’ Prosci Change Practitioner (for governance + adoption work) β€’ SAFe Practitioner (SP) or Scrum Master (PSM I / CSM) β€’ Azure Fundamentals (AZ-900) β€’ Security+ or ISCΒ² CC (for data governance and disclosure related work) Roles and Responsibilities: Personnel Director of Data and Analytics will: β€’ Provide an introductory process β€’ Assign work β€’ Set the schedule β€’ Provide high-level oversight β€’ Provide performance feedback β€’ Establish a deliverable review process β€’ Approve deliverables submitted for work performed β€’ Establish invoice process β€’ Approve invoices submitted for work performed β€’ Provide necessary resources β€’ Resolve issues Key Deliverables: β€’ Data Quality Rule Library, including governed and version-controlled downstream data quality rules for completeness, validity, conformity, thresholds, exception logic, steward ownership, business term links, and lineage references. β€’ Data Quality Validation Framework, including validation logic, runbooks, troubleshooting guidance, pipeline output requirements, manifests, record counts, and schema metadata expectations. β€’ Standardized Dashboards, including internal dashboards with consistent design standards, data quality overlays, SPC indicators, KPI documentation, data sources, and suppression behavior. β€’ Change Management and Sustainment Materials, including readiness assessments, adoption dashboards, communications, sustainment roadmap, and support for long-term staff independence. --- Thanks Srujana