
Azure Data Engineer
Brillio
full-time
Posted on:
Location Type: Hybrid
Location: Bangalore • India
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About the role
- Design, build, and maintain ETL/ELT pipelines using Synapse or Azure Data Factory.
- Implement data models and optimize storage for Azure Data Lake Storage (ADLS), Azure SQL, and Azure Synapse SQL.
- Write clean, performant PySpark/SQL transformations; manage job orchestration, dependencies, and retries.
- Develop and maintain Azure Functions for lightweight data transformations, event-driven ingestion, and custom pipeline activities.
- Use Azure Logic Apps to build automated workflows for data movement, validations, notifications, and approvals.
- Develop batch pipelines and streaming jobs using Azure Event Hubs / Kafka and Structured Streaming or Azure Stream Analytics.
- Contribute to data pipeline deployments using Azure DevOps (Repos, Pipelines), version control (Git), and environment management.
- Implement data validation, profiling, and monitoring (e.g., ADF/Synapse job monitoring, alerting).
- Apply best practices for RBAC, Key Vault, Managed Identities, and data governance (cataloging, lineage) using Microsoft Purview.
- Produce clear technical documentation and collaborate with BI/ML teams to support use cases.
Requirements
- 3–5 years of hands-on experience building reliable, scalable data pipelines on Microsoft Azure
- 2–3 years experience in data engineering with Azure.
- Strong problem-solving, data analysis, and root cause analysis skills
- Strong knowledge of Azure Data Factory or Azure Synapse Analytics.
- Proficiency in PySpark, SQL, and performance tuning.
Benefits
- Azure ADF Data Engineers.
- Health insurance
- Retirement plans
- Flexible work arrangements
- Professional development
- Stock options
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
ETLELTPySparkSQLAzure Data FactoryAzure Synapse SQLAzure FunctionsAzure Logic AppsAzure Event HubsKafka
Soft Skills
problem-solvingdata analysisroot cause analysis