Chief Data Architect

4 days ago

Hubli, Karnataka, India beBeeDataArchitecture Full time ₹ 18,00,000 - ₹ 20,00,000

Job Summary:

We are seeking a highly skilled Chief Data Architect to lead our data engineering efforts and drive business growth through strategic data management.

About the Role:

  • The ideal candidate will have extensive experience in designing and implementing scalable data pipelines, working with streaming data, and collaborating with cross-functional teams to deliver high-quality solutions.

Key Responsibilities:

  • Design, build, and optimize data architectures and ETL workflows to ensure data quality, scalability, and performance.
  • Manage and process large datasets using Spark, PySpark, and SQL, ensuring seamless integration with AI/ML models.
  • Develop and maintain real-time streaming applications with Spark Streaming/Kafka, ensuring low-latency and high-throughput data processing.
  • Collaborate with data scientists and product teams to integrate AI/ML models into production, driving business outcomes through data-driven decision-making.
  • Ensure data governance, security, and compliance across all data pipelines, adhering to industry standards and regulations.
  • Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks), optimizing resource utilization and cost-effectiveness.
  • Automate testing and deployment of Spark jobs via CI/CD pipelines, ensuring continuous delivery and monitoring.

Requirements:

  • Bachelor's/Master's degree in Computer Science, Computer Engineering, or related field.
  • Minimum 8 years of professional experience as a Data Engineer or equivalent role.
  • Strong expertise in Python, Spark, PySpark, and cloud-based technologies (AWS, Azure, GCP).
  • Hands-on experience with Spark SQL, DataFrames, UDFs, DAG execution, and distributed computing.
  • Knowledge of data ingestion tools (Kafka, Flume, Kinesis) and data formats (Parquet, Avro, ORC).
  • Proficiency in Airflow/Prefect for scheduling and orchestration, and experience with CI/CD pipelines (Jenkins, GitHub Actions).
  • Familiarity with performance tuning in Spark (partitioning, caching, broadcast joins), and experience with troubleshooting complex data pipeline issues.

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