Azure Cloud Data Engineer

Persistent Systems; Pune District; Hybrid; INR 2,000,000 - 4,000,000; Full time

Benefits offered by this job

Education sponsorship

Long service awards

Insurance coverage

Job summary

Persistent Systems is seeking a highly skilled Azure Cloud Data Engineer to design and optimize enterprise-scale data platforms on Microsoft Azure. The role focuses on Azure Databricks, Spark, PySpark, Kafka, Python, and modern data engineering practices for both batch and real-time workloads.

Responsibilities include building scalable pipelines, implementing ETL/ELT, and ensuring data quality across platforms.

Qualifications

• 6–8 years of experience in Data Engineering and Cloud Data Platforms.
• Proficient with PySpark and Azure Databricks.
• Strong programming skills in Python and Spark ecosystem.
• Experience with streaming architectures using Kafka.

Responsibilities

• Design, develop, and maintain scalable data pipelines on Azure Databricks and Spark.
• Build batch and streaming ingestion frameworks for enterprise-scale data processing.
• Develop ETL/ELT solutions for structured and unstructured data sources.
• Implement data transformation, validation, and enrichment processes.
• Develop real-time data processing solutions using Kafka and event-driven architectures.
• Manage and optimize Databricks clusters for performance, scalability, and cost efficiency.

Skills

PySpark

Azure Databricks

Apache Spark

Kafka

Delta Lake

Python

Tools

Azure Databricks

Apache Spark

Kafka

Delta Lake

Azure Data Factory

Azure DevOps

Job description

We are seeking a highly skilled Azure Cloud Data Engineer with strong expertise in designing, developing, and optimizing enterprise-scale data platforms on Microsoft Azure. The ideal candidate will have hands‑on experience in Azure Databricks, Apache Spark, PySpark, Kafka, Python/Scala, and modern Data Engineering practices supporting both batch and real‑time data processing workloads.

• Experience: 6 to 8 Years
• Job Type: Full Time Employment

What You'll Do:

• Design, develop, and maintain scalable data pipelines using Azure Databricks, Apache Spark, and PySpark.
• Build robust batch and streaming data ingestion frameworks for enterprise-scale data processing.
• Develop ETL/ELT solutions for structured, semi‑structured, and unstructured data sources.
• Implement data transformation, enrichment, cleansing, and validation processes.
• Develop real‑time data processing solutions using Kafka and event‑driven architectures.
• Design and implement streaming pipelines supporting low‑latency analytics and business use cases.
• Work with large‑scale datasets and optimize distributed data processing workloads.
• Support real‑time monitoring and operational reporting capabilities.
• Build and manage cloud‑native data solutions on Microsoft Azure.
• Develop solutions leveraging Azure Databricks and modern Lakehouse architectures.
• Manage and optimize Databricks clusters for performance, scalability, and cost efficiency.
• Utilize Delta Lake and cloud storage solutions to support analytics and reporting workloads.
• Ensure data quality, integrity, consistency, and reliability across data platforms.
• Implement reconciliation, validation, monitoring, and exception handling frameworks.
• Troubleshoot pipeline failures and drive root cause resolution.
• Support governance, security, and compliance requirements for enterprise data environments.
• Collaborate with Architects, Data Scientists, Analysts, Business Stakeholders, and Engineering Teams.
• Translate business requirements into scalable technical solutions.
• Participate in design discussions, code reviews, and architecture reviews.
• Support Agile delivery and continuous improvement initiatives.

Expertise You'll Bring:

• 6-8 years of experience in Data Engineering and Cloud Data Platforms.
• PySpark
• Experience building enterprise‑scale data processing solutions on Microsoft Azure.
• Strong understanding of cloud‑native data platform architectures.
• Strong programming skills in Python
• PySpark
• Scala (Preferred)
• Experience developing scalable and reusable data engineering frameworks.
• Strong coding, debugging, and performance optimization capabilities.
• Experience implementing Data Validation
• Monitoring
• Alerting
• Strong understanding of production support and operational excellence practices.
• Experience with Delta Lake and Lakehouse architectures.
• Exposure to Azure Data Factory (ADF).
• Knowledge of CI/CD pipelines and DevOps practices.
• Familiarity with Azure DevOps, Git, and release automation.
• Experience supporting Analytics, AI, and Machine Learning initiatives.
• Understanding of cloud security and governance standards.
• Competitive salary and benefits package
• Culture focused on talent development with quarterly growth opportunities and company‑sponsored higher education and certifications
• Opportunity to work with cutting‑edge technologies
• Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards
• Insurance coverage: group term life, personal accident, and Mediclaim hospitalisation for self, spouse, two children, and parents

Values-Driven, People-Centric & Inclusive Work Environment:

Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds.

• We support hybrid work and flexible hours to fit diverse lifestyles.
• Our office is accessibility‑friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities.
• If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment.

"Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."