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3 days ago
This is an exciting opportunity to work as a Data Engineer, driving data-driven initiatives forward. The role involves building scalable pipelines, working with streaming data, and collaborating with cross-functional teams.
Key Responsibilities:- Data Pipeline Development: Design, build, and optimize data pipelines and ETL workflows to ensure efficient data processing.
- Big Data Management: Manage and process large datasets using Spark, PySpark, and SQL to drive business insights.
- Streaming Applications: Build and maintain real-time streaming applications with Spark Streaming/Kafka for immediate business impact.
- AI/ML Integration: Collaborate with data scientists and product teams to integrate AI/ML models into production, enhancing our data-driven decision-making capabilities.
- Pipeline Optimization: Ensure data quality, scalability, and performance in all pipelines to meet evolving business needs.
- Cloud Deployment: Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks) for seamless scalability.
- 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 strong expertise in Python, Spark, PySpark.
- Technical Skills: Hands-on experience with Spark SQL, DataFrames, UDFs, DAG execution, knowledge of data ingestion tools (Kafka, Flume, Kinesis), data formats (Parquet, Avro, ORC), proficiency in Airflow/Prefect for scheduling and orchestration, familiarity with performance tuning in Spark (partitioning, caching, broadcast joins).
- Deployment Experience: Experience deploying on Databricks, AWS EMR, Azure HDInsight, or GCP Dataproc.
- Testing and CI/CD: Exposure to testing and CI/CD for data pipelines (pytest, Jenkins, GitHub Actions).
This role requires strong analytical, problem-solving, and communication skills to drive success in this Data Engineer position.
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