Key Responsibilities and Required Skills for Azure Data Engineer

🎯 Role Definition

We are seeking an experienced Azure Data Engineer to design, build, operate, and optimize scalable cloud-native data platforms and pipelines on Microsoft Azure. The ideal candidate will translate business requirements into robust ETL/ELT workflows, data models, and data lake/warehouse solutions using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, ADLS Gen2 and related services. This role emphasizes end-to-end ownership: ingestion, transformation, storage, orchestration, monitoring, security, and cost optimization while collaborating closely with analytics, data science, and product teams.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Engineer / ETL Developer
BI Developer or SQL Developer with cloud exposure
Data Analyst transitioning to engineering

Advancement To:

Senior Azure Data Engineer / Lead Data Engineer
Data Engineering Manager / Technical Architect
Cloud Data Platform Architect

Lateral Moves:

Data Architect
Machine Learning Engineer
Analytics Engineering / Product Analytics Lead

Core Responsibilities

Primary Functions

Design, build and maintain scalable, production-grade ETL/ELT pipelines using Azure Data Factory and Azure Databricks to ingest structured and unstructured data from on-premise and cloud sources.
Architect and implement data warehousing solutions using Azure Synapse Analytics, including dedicated SQL pools, serverless SQL pool development, distribution strategies and partitioning to support high-performance analytics.
Develop and optimize Apache Spark jobs in Azure Databricks (PySpark/Scala) for large-scale transformation, enrichment and feature preparation while minimizing compute costs.
Manage and organize data lakes in ADLS Gen2, enforce folder structures, lifecycle policies, and optimize storage tiers to balance performance and cost.
Implement Delta Lake patterns on Databricks or Synapse to enable ACID transactions, schema evolution, time travel, and efficient incremental processing for batch and streaming workloads.
Build streaming ingestion and processing architectures using Azure Event Hubs, Kafka, Azure Stream Analytics or Databricks Structured Streaming for low-latency event processing scenarios.
Create robust data ingestion patterns including CDC (Change Data Capture), bulk loads, incremental pipelines and event-driven architectures to ensure near-real-time data availability.
Design and implement secure, auditable data access using Azure RBAC, managed identities, service principals, private endpoints, data encryption (at rest and in transit), and role-based access control for sensitive datasets.
Implement logging, monitoring and alerting for pipelines and clusters using Azure Monitor, Log Analytics, Databricks metrics, Application Insights and custom SLA dashboards to ensure reliability and rapid incident response.
Author and maintain modular, parameterized pipeline templates and infrastructure-as-code (IaC) using ARM templates, Bicep or Terraform for reproducible deployments across environments.
Implement CI/CD pipelines for data platform artifacts (ADF pipelines, Databricks notebooks, SQL scripts) using Azure DevOps or GitHub Actions to enforce code review, testing and repeatable deployments.
Design and maintain canonical data models, dimensional models and semantic layers to support BI tools like Power BI and downstream analytics consumption.
Develop and enforce data quality frameworks, automated validation, anomaly detection and reconciliation processes using unit tests, data quality tools, or custom solutions to guarantee trustworthy datasets.
Collaborate closely with Data Scientists, Analysts and Product Owners to translate analytics requirements into data engineering tasks, data contracts and data schemas.
Perform query performance tuning, index/partition optimization and workload management for Synapse SQL pools and Azure SQL databases to meet latency and concurrency SLAs.
Ensure compliance with data governance, lineage and cataloging policies by integrating with tools like Microsoft Purview, documenting data lineage and maintaining metadata for discoverability.
Lead architecture and design reviews, participate in sprint planning, conduct code reviews and enforce coding standards across the data engineering team.
Troubleshoot production incidents, perform root-cause analysis, implement corrective actions and maintain runbooks and postmortem documentation.
Estimate work, create technical designs and produce up-to-date documentation (data dictionaries, pipeline diagrams, runbooks) to support maintainability and knowledge transfer.
Optimize cloud costs by selecting appropriate compute tiers, right-sizing clusters, scheduling job runtimes, using spot instances where appropriate and recommending architectural trade-offs.
Mentor junior engineers, provide technical guidance on best practices for Spark, SQL, pipeline orchestration and secure cloud operations.
Integrate with streaming and messaging platforms as needed (Event Hubs, Kafka, Service Bus) and implement exactly-once or at-least-once processing semantics where required.
Implement backup, disaster recovery and retention strategies for critical datasets and data stores, and validate recovery procedures regularly.
Collaborate with security, legal and compliance teams to implement policies for data residency, PII/PHI handling, masking and anonymization where necessary.

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.

Required Skills & Competencies

Hard Skills (Technical)

Azure Data Factory: pipeline authoring, mapping data flows, triggers, integration runtime configuration and performance tuning.
Azure Databricks: notebook development (PySpark/Scala), cluster configuration, job orchestration and Delta Lake implementation.
Azure Synapse Analytics / Azure SQL: data warehouse design, T-SQL tuning, materialized views and workload isolation (resource classes).
ADLS Gen2: secure data lake design, ACLs, hierarchical namespace, lifecycle management and cost optimization.
SQL and T-SQL: complex query writing, window functions, performance tuning and query plan analysis.
Python and Spark development: ETL/ELT logic, UDFs, efficient joins, partitioning strategies and memory management.
Streaming technologies: Azure Event Hubs, Kafka, Stream Analytics, Structured Streaming for real-time ingestion and processing.
Infrastructure as Code / DevOps: ARM/Bicep/Terraform, Azure DevOps/GitHub Actions, CI/CD pipelines for automated deployments.
Data modeling and dimensional design: star schema, slowly changing dimensions, normalization vs. denormalization and conformed dimensions.
Data governance, cataloging and lineage: Microsoft Purview or similar tools, metadata management and data classification.
Security & compliance: RBAC, managed identities, network isolation, encryption and GDPR/CCPA-aware data handling.
Monitoring and observability: Azure Monitor, Log Analytics, Databricks metrics, custom dashboards and SLA management.
Performance tuning and cost optimization for cloud compute and storage, cluster autoscaling and job scheduling strategies.
Messaging and integration: REST APIs, Azure Functions, Logic Apps, Service Bus integration patterns.
Knowledge of BI tools: Power BI integration, semantic modeling (Tabular), and support for dashboarding and self-service analytics.

Soft Skills

Excellent verbal and written communication to translate technical solutions for non-technical stakeholders.
Strong problem-solving and analytical mindset with attention to detail.
Proven ability to work in Agile / Scrum environments and manage competing priorities.
Collaborative team player who can partner with data scientists, analysts, and business owners.
Mentorship and leadership capability to coach junior engineers and improve team delivery.
Customer-focused approach and ability to gather requirements effectively.
Time management and self-organization across multiple concurrent projects.
Continuous learning mindset to stay current with evolving Azure data services and best practices.
Strong documentation skills for runbooks, architecture diagrams and data dictionaries.
Resilience and troubleshooting under pressure during production incidents.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Software Engineering, Data Science, Mathematics, Statistics, or a related technical discipline; or equivalent practical experience.

Preferred Education:

Master’s degree in Data Engineering, Computer Science, Analytics, or related field.
Microsoft certifications such as Microsoft Certified: Azure Data Engineer Associate (DP-203) or Azure Solutions Architect.

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Analytics
Information Systems
Mathematics / Statistics
Cloud Computing / Engineering

Experience Requirements

Typical Experience Range: 3–8 years building and operating data platforms and ETL/ELT pipelines; at least 2–4 years specifically on Microsoft Azure data services.

Preferred:

5+ years of data engineering experience with demonstrable projects built on Azure.
Direct, hands-on experience with Azure Data Factory, Azure Databricks, Azure Synapse Analytics, ADLS Gen2 and CI/CD in Azure DevOps or GitHub.
Proven track record of architecting end-to-end data solutions, optimizing Spark jobs and SQL warehouses, and implementing data governance and security controls.