Scalable Data Architect

5 days ago

Anand, Gujarat, India beBeeDataEngineering Full time ₹ 18,00,000 - ₹ 25,00,000

Job Title: Data Engineer

We are seeking an experienced professional to lead the development of our data infrastructure. This role involves designing and implementing scalable data pipelines, working with streaming data, and collaborating with cross-functional teams to deliver high-quality solutions.

Key Responsibilities:

  • Design and optimize data pipelines and ETL workflows for maximum efficiency and scalability.
  • Process large datasets using Spark, PySpark, and SQL, ensuring accuracy and reliability in all data operations.
  • Built real-time streaming applications with Spark Streaming/Kafka, providing timely insights for business stakeholders.
  • Collaborate with data scientists and product teams to integrate AI/ML models into production environments.
  • Evaluate and ensure data quality, scalability, and performance in all pipelines, identifying areas for improvement and optimization.
  • Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks), ensuring seamless integration and operation.
  • Automate testing and deployment of Spark jobs via CI/CD pipelines, streamlining the development process and reducing errors.

Requirements:

  • Bachelor's/Master's degree in Computer Science, Computer Engineering, or related field, demonstrating strong technical foundations.
  • A minimum of 4 years of professional experience as a Data Engineer, with a proven track record of delivering complex projects.
  • Strong expertise in Python, Spark, PySpark, and hands-on experience with Spark SQL, DataFrames, UDFs, and DAG execution.
  • Knowledge of data ingestion tools (Kafka, Flume, Kinesis) and data formats (Parquet, Avro, ORC), ensuring efficient data processing and storage.
  • Proficiency in Airflow/Prefect for scheduling and orchestration, streamlining workflow management.
  • Familiarity with performance tuning in Spark (partitioning, caching, broadcast joins), optimizing data operations for maximum efficiency.
  • Experience deploying on Databricks, AWS EMR, Azure HDInsight, or GCP Dataproc, ensuring compatibility and operability across multiple platforms.
  • Exposure to testing and CI/CD for data pipelines (pytest, Jenkins, GitHub Actions), guaranteeing robustness and reliability in all data operations.

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