AI Data Ops Engineer
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Key skills for this role
About the Role
A leading Abu Dhabi-based holding group is hiring an AI Data Ops Engineer to engineer pipelines for AI assistants and enterprise AI products. The role involves building batch and stream pipelines, implementing data quality, and reviewing vendor-delivered data architectures.
Key Skills for This Role
Responsibilities
- Engineer batch and stream pipelines using Fabric Data Pipelines / Azure Data Factory, Synapse, or Databricks.
- Implement data quality rules, schema validation, de duplication, SCD, and reconciliation checks.
- Operationalize lineage, cataloging, and classifications with Microsoft Purview; enforce RBAC and access patterns.
- Automate CI/CD via Azure DevOps or GitHub with environment promotion, infrastructure as code (Bicep/Terraform), and secrets management via Key Vault.
- Build feature stores and model serving data contracts in partnership with MLOps and AI engineering teams.
- Own reliability: alerts, runbooks, on call rotation, and cost and performance optimization.
- Review and finalize vendor delivered data pipelines and data architecture for AI projects; ensure compliance with client standards for security, performance, and reliability; approve production readiness.
- Collaborate with delivery partner squads on interface specifications, test data, and delivery checkpoints; support SIT/UAT and production cutover.
- Define and enforce Data Contracts and SLAs per priority dataset (schema, refresh frequency, quality thresholds, reconciliation checks, and consumer expectations).
- Own data incident management: classification, RCA, corrective actions, and prevention of recurring nonconformities.
- Formalize the handshake with AI/ML/DevOps engineers on feature and embedding pipelines, monitoring hooks, and release gates for data dependent AI deployments.
Requirements
- 6 8 years in data engineering with strong SQL and PySpark and cloud native data services
- Hands on experience across the Azure data stack: Fabric/Synapse, Data Factory, ADLS Gen2, Delta Lake/Parquet
- Bachelor's in Computer Science, Engineering, or equivalent
- Python and PySpark, SQL, Lakehouse patterns, medallion architecture
- Microsoft Purview: catalog, lineage, classifications; data privacy controls and masking
- CI/CD with Azure DevOps or GitHub; IaC with Bicep or Terraform; Docker basics
- Observability: Kusto/KQL, Azure Monitor, Log Analytics; performance tuning and FinOps
- Production incident response and RCA discipline
- Microsoft Certified: Azure Data Engineer Associate (DP 203)
Full Job Posting
Role Overview
- We're hiring an AI Data Ops Engineer for a leading Abu Dhabi based holding group investing heavily in its data and AI capability.
- You'll engineer the pipelines that feed everyday AI assistants and enterprise AI products — and you'll be the technical authority reviewing and signing off vendor delivered data architectures before they reach production.
- Reports to the AI Solutions Manager.
What You'll Own
- Engineer batch and stream pipelines using Fabric Data Pipelines / Azure Data Factory, Synapse, or Databricks.
- Implement data quality rules, schema validation, de duplication, SCD, and reconciliation checks.
- Operationalize lineage, cataloging, and classifications with Microsoft Purview; enforce RBAC and access patterns.
- Automate CI/CD via Azure DevOps or GitHub with environment promotion, infrastructure as code (Bicep/Terraform), and secrets management via Key Vault.
- Build feature stores and model serving data contracts in partnership with MLOps and AI engineering teams.
- Own reliability: alerts, runbooks, on call rotation, and cost and performance optimization.
- Review and finalize vendor delivered data pipelines and data architecture for AI projects; ensure compliance with client standards for security, performance, and reliability; approve production readiness.
- Collaborate with delivery partner squads on interface specifications, test data, and delivery checkpoints; support SIT/UAT and production cutover.
- Define and enforce Data Contracts and SLAs per priority dataset (schema, refresh frequency, quality thresholds, reconciliation checks, and consumer expectations).
- Own data incident management: classification, RCA, corrective actions, and prevention of recurring nonconformities.
- Formalize the handshake with AI/ML/DevOps engineers on feature and embedding pipelines, monitoring hooks, and release gates for data dependent AI deployments.
What You Bring
- 6–8 years in data engineering with strong SQL and PySpark and cloud native data services.
- Hands on experience across the Azure data stack: Fabric/Synapse, Data Factory, ADLS Gen2, Delta Lake/Parquet.
- Bachelor's in Computer Science, Engineering, or equivalent.
Core Skills and Tools Required
- Python and PySpark, SQL, Lakehouse patterns, medallion architecture.
- Microsoft Purview: catalog, lineage, classifications; data privacy controls and masking.
- CI/CD with Azure DevOps or GitHub; IaC with Bicep or Terraform; Docker basics.
- Observability: Kusto/KQL, Azure Monitor, Log Analytics; performance tuning and FinOps.
- Production incident response and RCA discipline.
Required Certifications
- Microsoft Certified: Azure Data Engineer Associate (DP 203)
Preferred Certifications
- Microsoft Certified: Azure Fundamentals (AZ 900)
- Databricks Data Engineer Associate or Professional
Salary Range
- 15,000 23,000 AED per month
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