Chief Data Workflow Architect

1 day ago

Thiruvananthapuram, Kerala, India beBeeDataEngineer Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Job Overview

As a Senior Data Engineer, you will be responsible for developing and implementing scalable real-time and batch processing workflows using Azure Databricks, PySpark, and Apache Spark.

You will focus on data pre-processing, including cleaning, transformation, deduplication, normalization, encoding, and scaling to ensure high-quality input for downstream analytics.

Design and maintain cloud-based data architectures, including data lakes, lakehouses, and warehouses, following best practices in data engineering.

Deploy and optimize data solutions on Azure (preferred), AWS, or GCP with a focus on performance, security, and scalability.

Develop and optimize ETL/ELT pipelines for structured and unstructured data from IoT, MES, SCADA, LIMS, and ERP systems.

Automate data workflows using CI/CD and DevOps best practices, ensuring security and compliance with industry standards.

Monitor, troubleshoot, and enhance data pipelines for high availability and reliability.

Utilize Docker and Kubernetes for scalable data processing.

Collaborate with automation team, data scientists, and engineers to provide clean, structured data for AI/ML models.

Key Responsibilities
  • Build scalable real-time and batch processing workflows using Azure Databricks, PySpark, and Apache Spark.
  • Perform data pre-processing tasks, including data cleaning, transformation, deduplication, normalization, encoding, and scaling.
  • Design and maintain cloud-based data architectures, including data lakes, lakehouses, and warehouses.
  • Deploy and optimize data solutions on cloud platforms.
  • Develop and optimize ETL/ELT pipelines for structured and unstructured data.
  • Automate data workflows using CI/CD and DevOps best practices.
  • Monitor, troubleshoot, and enhance data pipelines.
  • Collaborate with cross-functional teams to provide clean, structured data for AI/ML models.

Required Skills
  • PySpark and Azure Databricks development
  • Apache Spark programming
  • Cloud-based data architecture design
  • ETL/ELT pipeline development
  • CI/CD and DevOps best practices
  • Docker and Kubernetes deployment
  • Automation tools and scripting

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