Data engineer

2 weeks ago

Kurnool, 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)ExperienceMinimum 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 Py Spark (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 SkillsSpark Core API: RDDs, transformations/actions, DAG execution.Spark SQL: Data Frames, 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, Git Hub Actions.✅ Education & Soft SkillsBachelor’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 ResponsibilitiesDesign, build, and optimize data pipelines and ETL workflows.Manage and process large datasets using Spark, Py Spark, 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.RequirementsBachelor’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, Py Spark.Hands-on experience with Spark SQL, Data Frames, 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, Git Hub Actions).BudgetOnshore: As per market standards.

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