

My3Tech
DataOps Engineer/ Data Engineer
β - 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.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
-
ποΈ - Date
May 15, 2026
π - Duration
Unknown
-
ποΈ - Location
Remote
-
π - Contract
Unknown
-
π - Security
Unknown
-
π - Location detailed
Vermont, United States
-
π§ - 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
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






