Torchora
Back to Home

Key Responsibilities and Required Skills for Azure DevOps Data Engineer

💰 $ - $

Data EngineeringDevOpsAzure

🎯 Role Definition

The Azure DevOps Data Engineer is responsible for architecting, building, automating, and operating scalable, secure, and cost-effective data platforms and pipelines on Microsoft Azure. This role blends strong data engineering capabilities (ETL/ELT, Delta Lake, Spark, Synapse, ADF, Databricks) with DevOps and infrastructure-as-code expertise (Azure DevOps Pipelines, Git, Terraform/Bicep, ARM templates, CI/CD, automated testing, containerization) to deliver production-grade data solutions, enable self-service analytics, and ensure operational reliability, governance and observability across the data estate.

Key focus areas: CI/CD for data pipelines, automated deployment of data platform components, infrastructure-as-code, pipeline testing and validation, monitoring and alerting, security and compliance, cost optimization, collaboration with analytics and product teams, and mentoring junior engineers.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Engineer (ETL/ELT) transitioning into delivery automation and platform ownership
  • DevOps Engineer with hands-on experience in cloud infrastructure and scripting
  • BI Developer or Analytics Engineer with strong SQL, Python, and pipeline experience

Advancement To:

  • Senior Azure DevOps Data Engineer / Lead Data Engineer
  • Data Platform Architect / Cloud Data Architect
  • Principal Engineer / Head of Data Engineering

Lateral Moves:

  • Cloud Infrastructure Engineer (Azure Cloud Engineer)
  • Site Reliability Engineer (SRE) for data platforms
  • Machine Learning Engineer or MLOps Engineer focused on model pipelines

Core Responsibilities

Primary Functions

  • Design, implement, and maintain enterprise-grade CI/CD pipelines for data ingestion, transformation, and analytics workloads using Azure DevOps Pipelines (YAML), Git, and automated release management to ensure repeatable, auditable deployments to multiple environments.
  • Build, optimize, and operate end-to-end data engineering solutions on Azure including Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Blob/ADLS Gen2, Delta Lake, and SQL-based stores, ensuring high throughput and low latency for batch and streaming use cases.
  • Develop Infrastructure-as-Code templates using Terraform, Bicep, or ARM templates to provision and manage cloud resources (storage accounts, Databricks workspaces, Synapse workspaces, AKS, Key Vault), driving reproducibility, governance and cost control across environments.
  • Implement robust automated testing (unit tests, integration tests, data validation tests, regression tests) and quality gates in CI pipelines to prevent data quality regressions and to ensure pipeline integrity before promotion to production.
  • Design and enforce Git branching strategies, pull request workflows, and code review practices for data engineering repositories to maintain code quality, security, and team collaboration.
  • Author and maintain operational runbooks, deployment guides, and infrastructure diagrams; automate routine operational tasks (backups, schema migrations, incremental loads, CDC) to minimize manual intervention and MTTR (mean time to recovery).
  • Implement monitoring, alerting and observability for data pipelines and platform components using Azure Monitor, Log Analytics, Application Insights, Databricks metrics and custom telemetry to detect anomalies, capacity issues, and performance regressions.
  • Lead the design and implementation of secure data access patterns, integrating Azure AD, managed identities, Key Vault, RBAC, network security (VNETs, private endpoints) and encryption-at-rest/in-transit to meet security and compliance requirements (GDPR, HIPAA, SOC2).
  • Architect and implement scalable streaming and real-time ingestion solutions (Event Hubs, Kafka, Azure Stream Analytics, Databricks Structured Streaming) ensuring event ordering, exactly-once processing where required, and idempotent transformations.
  • Implement efficient partitioning, file-format choices (Parquet/Delta), compaction strategies, and table design (Delta Lake / Synapse dedicated SQL / serverless SQL) to optimize performance and control storage costs for large-scale analytics workloads.
  • Collaborate cross-functionally with data scientists, BI teams, product owners and platform engineers to translate business requirements into reliable, maintainable, and testable data pipelines and analytics platforms.
  • Tune Spark jobs, query performance, and resource allocation in Databricks and Synapse to reduce job failures and improve cost-performance trade-offs; implement autoscaling and pool management strategies.
  • Implement data lineage, metadata capture, and data cataloging integrations (Azure Purview, open metadata frameworks) to provide transparency, impact analysis and governance for producers and consumers of data assets.
  • Plan and execute migration and modernization initiatives to move legacy ETL jobs and data warehouses to cloud-native architectures including lift-and-shift and re-architecting phases with minimal business disruption.
  • Drive cost optimization initiatives: sizing resources, rightsizing dedicated pools, optimizing data storage lifecycle policies, and automating shutdown/start behaviors for non-production environments.
  • Troubleshoot complex production incidents, perform root cause analysis (RCA), and present remediation plans with lessons learned; participate in on-call rotations and incident response exercises.
  • Define and track platform SLAs/OLAs and data pipeline SLOs, implement synthetic checks and data quality monitoring (DQ checks) to ensure reliability and business trust in data.
  • Lead or participate in architecture and design reviews, ensuring solutions adhere to company standards for security, scalability, maintainability, observability, and cost-effectiveness.
  • Mentor and coach junior engineers on DevOps best practices, data engineering patterns, code quality, and cloud-native development, while promoting knowledge sharing and continuous improvement.
  • Integrate CI/CD and data pipelines with downstream BI and analytics tools (Power BI, Tableau) and support safe, automated data releases that enable timely analytics delivery.
  • Automate schema evolution handling, ETL/ELT orchestration, and backfill strategies to support changing business models and evolving data schemas without data loss.
  • Implement secrets management, policy-as-code, and compliance scanning in pipelines (e.g., static code analysis, container image scanning, SAST/DAST tooling) to reduce security risks in deployment artifacts.
  • Collaborate with platform and security teams to implement network architecture, private endpoints, firewall rules and secure service connectivity for hybrid and multi-tenant environments.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Produce and maintain clear technical documentation, runbooks, and onboarding materials for data platform users and stakeholders.
  • Assist in vendor evaluations, proof-of-concepts, and cost/benefit analyses for new data platform technologies and services.
  • Conduct periodic architecture and security reviews to validate compliance with cloud governance and enterprise standards.
  • Help define key performance indicators (KPIs) for data pipeline performance and adoption metrics for the analytics platform.
  • Facilitate knowledge transfer sessions and brown-bag talks to upskill teams on Azure data platform and DevOps practices.

Required Skills & Competencies

Hard Skills (Technical)

  • Azure Data Platform: Azure Data Factory, Azure Synapse Analytics (Serverless & Dedicated), Azure Databricks
  • DevOps & CI/CD: Azure DevOps Pipelines (YAML), Git, GitHub Actions knowledge beneficial, automated release management
  • Infrastructure-as-Code: Terraform and/or Bicep/ARM templates for provisioning and lifecycle management of Azure resources
  • Big Data & ETL: Spark (PySpark/Scala), Delta Lake, Parquet, ELT/ETL patterns, CDC (Change Data Capture)
  • Programming & Scripting: Python, SQL (T-SQL), experience with Scala or Java is a plus
  • Orchestration & Scheduling: Azure Data Factory, Databricks Jobs, Airflow experience is a plus
  • Observability & Monitoring: Azure Monitor, Log Analytics, Application Insights, Databricks metrics, Prometheus/Grafana exposure beneficial
  • Security & Governance: Azure AD, Managed Identities, Azure Key Vault, RBAC, network security (VNETs, Private Endpoints), data privacy/compliance controls
  • Containerization & Kubernetes: Docker, AKS or Kubernetes basics for containerized data workloads
  • Testing & Quality: Automated testing frameworks, data validation frameworks, unit and integration tests for pipelines
  • Performance Tuning: Spark optimization, query tuning, partitioning strategies, resource configuration
  • Data Modeling & Warehousing: Star/snowflake modeling, dimensional modeling, data vault concepts, hands-on with relational and cloud-native warehouses
  • Cost Management: Azure Cost Management, chargeback/showback, autoscaling strategies and resource right-sizing
  • CI/CD Security: SAST/DAST, container image scanning, secrets management integrated into pipelines

Soft Skills

  • Strong collaboration and stakeholder management with technical and non-technical teams.
  • Excellent written and verbal communication for documenting runbooks, architecture decisions, and onboarding materials.
  • Problem-solving and analytical thinking, with a bias for automation and repeatability.
  • Pragmatic decision-making balancing speed, quality, cost and risk in cloud-native data platform engineering.
  • Mentorship and coaching mindset to uplift team capabilities and adopt DevOps best practices.
  • Adaptability to rapidly changing priorities and the ability to work in both hands-on and architectural capacities.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Mathematics, Statistics, or related technical field.

Preferred Education:

  • Master’s degree in Computer Science, Data Engineering, Cloud Computing, or related discipline and relevant professional certifications (e.g., Microsoft Certified: Azure Data Engineer Associate, Microsoft Certified: Azure DevOps Engineer Expert, Terraform Associate).

Relevant Fields of Study:

  • Computer Science
  • Data Engineering / Data Science
  • Software Engineering
  • Information Systems
  • Mathematics / Statistics

Experience Requirements

Typical Experience Range:

  • 3–8 years of hands-on experience in data engineering or cloud engineering roles; 3+ years specifically with Azure data services and 2+ years in DevOps/CI-CD pipeline development for data workloads.

Preferred:

  • 5+ years of combined data engineering and DevOps experience, demonstrable experience building CI/CD pipelines for data platforms, provisioning infrastructure with Terraform/Bicep, and production-operating Databricks/Synapse or equivalent cloud data services.
  • Prior experience with data governance, compliance frameworks, and production incident management; experience in mentoring engineers and leading cross-functional delivery initiatives.
Explore More Careers