Lead Data Engineer

About this role

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Skills you'll use

What you'll do

• Design and build scalable batch and real-time data pipelines using Spark, Kafka, and (preferred) Apache Flink
• Develop robust ETL/ELT frameworks for structured and unstructured data
• Build and optimize data ingestion and transformation pipelines for Data Lakes and Data Warehouses
• Implement stream processing solutions for near real-time use cases
• Ensure data quality, lineage, observability, and governance across pipelines
• Optimize data jobs for performance, scalability, and cost efficiency
• Design and operate cloud-native data platforms on AWS, Azure, or GCP
• Leverage managed services such as S3/ADLS/GCS, EMR/Databricks, BigQuery/Redshift/Snowflake
• Implement Infrastructure as Code (Terraform, CloudFormation, or equivalent)
• Ensure high availability, fault tolerance, and disaster recovery

Who can apply

• Strong proficiency in Object-Oriented Programming and Design (OOP/OOAD) Java (JDK 8+); Python and/or Go is a plus
• Experience building data services and distributed systems
• Strong understanding of multithreading, scalability, and performance tuning
• Strong hands-on experience with AWS, Azure, or GCP
• Experience with cloud-native data services (S3, ADLS, GCS, Databricks, EMR, BigQuery, Redshift)
• Strong experience with Apache Spark (Core, SQL, Structured Streaming)
• Hands-on experience with Kafka or equivalent messaging platforms
• Experience with real-time processing frameworks (Apache Flink preferred or Spark Streaming)
• Strong understanding of ETL/ELT design patterns and pipeline architectures
• Experience with data formats (Parquet, Avro, ORC)

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