Chief Data Architect

2 weeks ago

Amrāvati, Maharashtra, India beBeeDataEngineer Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
About the Position:

We are seeking a skilled data engineer to join our organization and contribute to designing, developing, and managing data pipelines and transformation frameworks. The ideal candidate will play a critical role in enabling analytics, reporting, and business intelligence capabilities across the enterprise.

Key Responsibilities:
  • Design and build scalable ETL/ELT pipelines using Azure Data Factory (ADF), Azure Databricks (Spark), and Azure Synapse Analytics.
  • Develop and optimize PySpark/Spark SQL jobs for large-scale batch and streaming data transformations.
  • Ingest data from various sources including Apache Kafka, REST APIs, and RDBMS, ensuring real-time or near-real-time availability.
  • Implement data modeling strategies (star schema, snowflake schema) for analytics consumption layers in Synapse or ADLS.
  • Collaborate with DevOps teams to automate deployment using CI/CD pipelines (Azure DevOps, GitHub Actions, etc.).
  • Monitor, troubleshoot, and optimize data workflows for performance, cost-efficiency, and reliability.
  • Follow coding standards, participate in peer reviews, and maintain version-controlled code in Git repositories.
  • Support data quality checks, logging, alerting, and observability mechanisms for production workloads.
  • Participate in sprint ceremonies and contribute to task estimation and delivery planning.
Requirements:
  • 5+ years of experience in data engineering roles.
  • Hands-on experience with:
    • Azure Data Factory (ADF) – building pipelines, triggers, linked services.
    • Azure Databricks – building and managing Spark jobs in PySpark.
    • Azure Synapse Analytics – data warehousing, SQL queries, workspace orchestration.
    • Apollo Kafka – consuming and processing real-time data streams.
  • Strong in SQL, Python, and Spark for data manipulation and transformation.
  • Exposure to CI/CD practices (Azure DevOps, Git workflows, build/release pipelines).
  • Understanding of data lake architecture and modern data warehousing principles.

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