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Thiruvananthapuram · On-site · 5-8 Yrs · Aug 23
Unknown · Bengaluru, India
9 open roles0 posted in 7d9 in 30d
Work-life balance 4.5Compensation & benefits 4.0Culture & values 4.5
Hiring: Engineering
What employees say
+ Good company to work for
− Great place if you are a performed
More reviews on the company profile.
Job description
Overview
Job Title: Senior Azure Data Engineer / Azure Data Engineer
Responsibilities
- We are looking for a Senior Azure Data Engineer with strong hands-on
- experience in Azure data services, data ingestion, and big data processing. The ideal candidate should have solid coding skills (PySpark, Python, SQL), deep understanding of Spark core, and strong communication abilities to work with cross-functional teams.
- Design, build, and maintain scalable data storage solutions using Azure SQL Database, Azure Data Lake, and Azure Blob Storage
- Develop and manage robust data pipelines for ingestion, processing, and transformation
- Create optimized data models to support analytics and reporting needs
- Build and maintain ETL/ELT pipelines using Azure Data Factory or similar tools
- Process and transform large-scale datasets using Azure Databricks and Apache Spark
- Implement data validation, cleansing, and quality checks to ensure data accuracy and reliability
- Ensure data security, governance, and compliance across the data platform
- Collaborate with data engineers, architects, and business stakeholders to translate requirements into scalable Azure data architectures
- Support CI/CD processes using Azure DevOps
Qualifications
- Notice Period: Immediate / Up to 15 Days
- 5+ years of experience as a Data Engineer, with strong focus on Azure
- Hands-on expertise in:
- Azure Data Factory
- Azure Databricks
- Azure Data Lake & Blob Storage
- Strong coding skills in:
- PySpark
- Python
- SQL
- Solid understanding of Apache Spark (core concepts & performance tuning)
- Exposure to Azure DevOps (CI/CD, pipelines)
- Strong analytical and problem-solving skills
- Good communication skills and ability to work with cross-functional teams
- Performance optimization for Spark and Databricks workloads
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