
Data Engineer
3 weeks ago
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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