Data Engineer

2 weeks ago

Rajahmundry, India R Systems Full time

Job Title: Data Engineer

Contract Period: 12 Months

Location: 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 Role

We 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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