High-Performance Data Architect

7 days ago

Bellary, Karnataka, India beBeeData Full time ₹ 40,00,000 - ₹ 50,00,000
Job Title: Data Engineer

The role of data engineer is pivotal in harnessing the potential of data to drive business forward. We are seeking an experienced professional with a strong background in designing, building, and optimizing data pipelines and ETL workflows.

About the Role:

This position involves working with streaming data, collaborating with data scientists and business stakeholders, and delivering high-quality solutions that meet organizational objectives.

  • Design, build, and optimize data pipelines and ETL workflows to ensure seamless data integration and processing.
  • Manage and process large datasets using Spark, PySpark, and SQL, ensuring data quality and scalability.
  • Build and maintain real-time streaming applications with Spark Streaming/Kafka, utilizing expertise in data ingestion tools and formats.
  • Collaborate with cross-functional teams to integrate AI/ML models into production, driving business value through data-driven insights.
  • Ensure data quality, scalability, and performance in all pipelines, leveraging knowledge of data warehousing and business intelligence.
  • Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks), maintaining high levels of efficiency and effectiveness.
  • Automate testing and deployment of Spark jobs via CI/CD pipelines, ensuring seamless deployment and minimizing downtime.

Requirements:

  • Bachelor's/Master's degree in Computer Science, Computer Engineering, or related field, demonstrating strong technical acumen.
  • Minimum 4 years of professional experience as a data engineer, showcasing expertise in data engineering principles and practices.
  • Strong expertise in Python, Spark, PySpark, with hands-on experience in Spark SQL, DataFrames, UDFs, DAG execution.
  • Knowledge of data ingestion tools (Kafka, Flume, Kinesis) and data formats (Parquet, Avro, ORC), with proficiency in Airflow/Prefect for scheduling and orchestration.
  • Familiarity with performance tuning in Spark (partitioning, caching, broadcast joins), with experience deploying on Databricks, AWS EMR, Azure HDInsight, or GCP Dataproc.
  • Exposure to testing and CI/CD for data pipelines (pytest, Jenkins, GitHub Actions), with a strong understanding of DevOps principles.

What You'll Need:

  • A bachelor's/master's degree in computer science, computer engineering, or a related field.
  • At least four years of experience as a data engineer.
  • Experience with Spark, PySpark, and other technologies listed above.
  • A solid understanding of data engineering principles and practices.

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