Principal Data Architect

3 days ago

Anantapur, Andhra Pradesh, India beBeeDataEngineer Full time US$ 1,20,000 - US$ 1,80,000
Job Opportunity

Data professionals with a passion for data engineering will find this role challenging and rewarding. As a seasoned Data Engineer, you will play a pivotal role in supporting data-driven initiatives by building scalable pipelines and collaborating with cross-functional teams to deliver high-quality solutions.

Detailed Job Responsibilities:
  • Data Pipeline Design and Development: Design, build, and optimize data pipelines and ETL workflows using industry-leading tools and technologies.
  • Data Processing and Management: Manage and process large datasets using Spark, PySpark, and SQL, ensuring data quality and integrity throughout the pipeline.
  • Real-Time Streaming Applications: Build and maintain real-time streaming applications with Spark Streaming/Kafka, enabling organizations to make data-driven decisions in real-time.
  • AI/ML Integration: Collaborate with data scientists and product teams to integrate AI/ML models into production, driving business value through predictive analytics.
  • Pipeline Optimization: Ensure data quality, scalability, and performance in all pipelines, leveraging expertise in data visualization and statistical analysis.
  • Cloud Deployment: Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks), taking advantage of elastic resources and auto-scaling capabilities.
  • CICD Pipelines: Automate testing and deployment of Spark jobs via CI/CD pipelines, streamlining development and deployment processes.
Key Requirements:
  • Education: Bachelor's/Master's degree in Computer Science, Computer Engineering, or related field.
  • Experience: Minimum 4 years of professional experience as a Data Engineer, with a strong track record of delivering complex data projects.
  • Technical Skills: Strong expertise in Python, Spark, PySpark, and hands-on experience with Spark SQL, DataFrames, UDFs, DAG execution.
  • Additional Skills: Knowledge of data ingestion tools (Kafka, Flume, Kinesis) and data formats (Parquet, Avro, ORC), proficiency in Airflow/Prefect for scheduling and orchestration, familiarity with performance tuning in Spark (partitioning, caching, broadcast joins).

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