Azure Data Engineer

Skill: Senior Data Engineer (L4) Python & Azure

Job Overview:

We are seeking a highly skilled Senior Data Engineer with deep expertise in

Microsoft Azure data ecosystem to design, develop, and maintain scalable data

pipelines and architectures. The ideal candidate will play a key role in building robust

data solutions that support advanced analytics, BI, and AI workloads across the

organization. This role involves working with cross-functional teams — data scientists,

analysts, and business stakeholders — to ensure high-quality, secure, and performant

data delivery.

Total experience : 8 to 10 years

Key Responsibilities:

1. Data Architecture & Pipeline Development

• Design, develop, and maintain data ingestion, transformation, and storage

pipelines using Azure Data Factory, Azure Databricks, and Synapse Analytics.

• Build end-to-end ETL/ELT workflows from diverse sources such as APIs, on-prem

databases, SaaS applications, and data lakes.

• Implement data modeling (star schema, snowflake, data vault, etc.) for analytical

and operational data stores.

• Manage data ingestion frameworks for both batch and streaming (real-time) use

cases using Azure Event Hubs / Azure Stream Analytics / Kafka.

2. Data Management & Governance

• Ensure data quality, consistency, and integrity across all environments.
• Implement and enforce data governance standards, including metadata

management, lineage tracking, and data cataloging (e.g., Azure Purview).

• Manage security, compliance, and access controls using Azure Active Directory

(AAD) and RBAC principles.

• Automate data validation, auditing, and monitoring workflows using modern

DevOps practices.

3. Cloud & DevOps Integration

• Manage and optimize Azure Data Lake Storage (ADLS Gen2) for cost and

performance.

• Leverage Infrastructure as Code (IaC) tools such as Terraform, Bicep, or ARM

templates for environment provisioning.

• Implement CI/CD pipelines for data workflows using Azure DevOps / GitHub

Actions.

• Optimize compute and storage costs while maintaining scalability and resilience.

4. Collaboration & Leadership

• Partner with data scientists, BI developers, and product teams to deliver reliable

data solutions.

• Mentor junior data engineers on best practices, coding standards, and Azure

services.

• Participate in architectural reviews and contribute to data platform design

decisions.

• Communicate technical insights and trade-offs to non-technical stakeholders

effectively.

Required Skills:

Core Azure Services:

• Azure Data Factory (ADF) – pipeline orchestration, triggers, linked services,

integration runtime.

• Azure Databricks – PySpark, Delta Lake, notebooks, job orchestration.
• Azure Synapse Analytics – dedicated and serverless SQL pools, data modeling,

query optimization.

• Azure Data Lake Storage (ADLS Gen2) – hierarchical namespace, lifecycle

management.

• Azure Functions / Logic Apps – event-driven data workflows.
• Azure Event Hubs / Kafka / Stream Analytics – real-time data ingestion.

Programming & Tools:

• Strong in Python or Scala for data transformations.
• Proficient in SQL (T-SQL, Spark SQL, or Synapse SQL).
• Experience with PySpark for distributed data processing.
• Knowledge of Git, CI/CD, and DevOps best practices.
• Familiarity with Power BI integration or other reporting tools is a plus.

Data Architecture & Modeling:

• Expertise in data warehouse design, data lakehouse architecture, and data

pipelines.

• Understanding of data partitioning, indexing, and query optimization

techniques.

• Experience with dimensional modeling, slowly changing dimensions, and fact

tables.

Other:

• Experience with API-based data ingestion and RESTful integrations.
• Exposure to machine learning data pipelines or feature stores is a plus.
• Familiarity with cost optimization and performance tuning in Azure.

Soft Skills

• Excellent analytical and problem-solving abilities.
• Strong communication and documentation skills.
• Ability to work in an agile, fast-paced environment.
• Proven experience collaborating in cross-functional teams.
• Self-driven with a passion for data engineering and cloud technologies.