Data Engineer

2 days ago

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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