Chief Data Architecture Specialist

3 days ago

Belgaum, Karnataka, India beBeeDataEngineer Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Senior Data Engineer Job Overview

This senior-level data engineer role involves the design and implementation of scalable, high-performance data pipelines using AWS cloud services and Databricks.

The ideal candidate will have strong expertise in designing and building ETL/ELT workflows for large-scale structured and unstructured datasets.

Key Responsibilities:

  • Data Pipeline Design and Development: Create and maintain data pipelines and workflows using AWS (S3, Redshift, Glue, EMR, Lambda, Kinesis, Athena) and Databricks.
  • ETL/ELT Process Optimization: Build and optimize ETL/ELT processes for large-scale structured and unstructured datasets.
  • Databricks Utilization: Leverage Databricks for data engineering, advanced analytics, and machine learning workloads.
  • Collaboration: Work closely with data scientists, analysts, and stakeholders to deliver reliable and scalable data solutions.
  • Data Governance and Monitoring: Implement data governance, quality checks, and monitoring frameworks to ensure high availability, scalability, and cost-effectiveness of data solutions.

Required Skills and Qualifications:

  • 7+ Years of Experience in Data Engineering: Proven experience in designing and implementing data pipelines and workflows.
  • AWS Cloud Services Expertise: Strong hands-on experience with AWS cloud services including S3, Redshift, Glue, EMR, Athena, Lambda, and Kinesis.
  • Databricks Expertise: Expertise in Databricks including PySpark, Delta Lake, and ML pipelines.
  • Programming Skills: Proficiency in Python and SQL for data transformation and querying.
  • Data Warehousing and Modeling: Strong understanding of data warehousing, data modeling, and big data frameworks.
  • Workflow Orchestration: Experience with workflow orchestration tools including Airflow and Step Functions.

Preferred Qualifications:

  • MLOps/ML Integration: Familiarity with MLOps/ML integration within Databricks.
  • Real-Time Data Processing: Experience with real-time/streaming data processing including Kafka and Spark Streaming.
  • Infrastructure as Code: Knowledge of infrastructure as code including Terraform and CloudFormation.
  • Compliance and Governance: Exposure to data security, compliance, and governance practices.

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