Data Engineer
5 days ago
**Responsibilities**:
- Architect and implement robust ETL/ELT pipelines to ingest and transform large volumes of data from heterogeneous systems (e.g., Mainframe, Oracle, SQL Server, DB2, Teradata) into distributed data platforms such as Hadoop and cloud-based data lakes.
- Utilize tools such as Apache Spark, PySpark, and Hive for batch and stream data processing, ensuring optimized performance and scalability across data workloads.
- Develop and manage data workflows on cloud platforms (primarily AWS EMR, S3, EC2)
- Integrate automation frameworks and parallel job orchestration using tools like AWS Step Functions and Control-M.
- Design and develop logical and physical data marts integrating data from multiple internal and external sources (e.g., Murex, Bloomberg, Refinitiv) to support business intelligence, reporting, and advanced analytics.
- Implement streaming data solutions using Kafka and Spark Streaming to enable real-time data ingestion, transformation, and transport across enterprise systems.
- Collaborate with QA teams to ensure data accuracy and reliability. Contribute to technical documentation, implementation guides, and standard operating procedures for streamlined delivery and operational support.
- Liaise with cross-functional teams including Product, Support, and Engineering to resolve issues, ensure alignment with business requirements, and deliver solutions within project timelines.
**Requirements**:
- Bachelor’s degree in Engineering, Computer Science, or a related field.
- At least 4-5 years of experience in Data engineering and ETL.
- At least 3 years of experience in Big Data technologies like Hadoop, Spark, Hive, Scala and Pyspark.
- Proficient in Azure and AWS services including EMR, S3, EC2, and Step Functions
- Proficiency in Python, SQL and Shell scripting.
- At least 1 year of experience in RPA/automation tools.
- Skilled in batch monitoring, incident management (BMC Remedy), and handling production environments.
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