Data Engineer

12 hours ago

Delhi, India R Systems Full time

Job Title: Data EngineerContract Period: 12 MonthsLocation: Offshore candidates accepted (Singapore Based Company)Work Timing : 6.30 AM to 3.30 PM or 7.00 AM to 4.00 PM (IST - India timing)Experience- Minimum 4+ years as a Data Engineer or similar role.(Please don't apply if less than 4 years exp in Data Engineer)- Proven experience in Python, Spark, and PySpark (non-negotiable).(mandatory*)- Hands-on in building ETL pipelines, real-time streaming, and data transformations.- Worked with data warehouses, cloud platforms (AWS/Azure/GCP), and databases.✅ Technical Skills- Spark Core API: RDDs, transformations/actions, DAG execution.- Spark SQL: DataFrames, schema optimization, UDFs.- Streaming: Structured Streaming, Kafka integration.- Data Handling: S3, HDFS, JDBC, Parquet, Avro, ORC.- Orchestration: Airflow/Prefect.- Performance Tuning: Partitioning, caching, broadcast joins.- Cloud Deployment: Databricks, AWS EMR, Azure HDInsight, GCP Dataproc.- CI/CD: Pytest/unittest for Spark jobs, Jenkins, GitHub Actions.✅ Education & Soft Skills- Bachelor’s/Master’s in Computer Science, Computer Engineering, or equivalent.- Strong analytical, problem-solving, and communication skills.About the RoleWe are seeking an experienced Data Engineer to join our team and support data-driven initiatives. This role involves building scalable pipelines, working with streaming data, and collaborating with data scientists and business stakeholders to deliver high-quality solutions.Key Responsibilities- Design, build, and optimize data pipelines and ETL workflows.- Manage and process large datasets using Spark, PySpark, and SQL.- Build and maintain real-time streaming applications with Spark Streaming/Kafka.- Collaborate with data scientists and product teams to integrate AI/ML models into production.- Ensure data quality, scalability, and performance in all pipelines.- Deploy and manage Spark workloads on cloud platforms (AWS, Azure, GCP, Databricks).- Automate testing and deployment of Spark jobs via CI/CD pipelines.Requirements- Bachelor’s/Master’s degree in Computer Science, Computer Engineering, or related field.- Minimum 4 years of professional experience as a Data Engineer.- Strong expertise in Python, Spark, PySpark.- Hands-on experience with Spark SQL, DataFrames, UDFs, DAG execution.- 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).- Experience deploying on Databricks, AWS EMR, Azure HDInsight, or GCP Dataproc.- Exposure to testing and CI/CD for data pipelines (pytest, Jenkins, GitHub Actions).Budget- Onshore: As per market standards.

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