4–7 yrsHyderabadOn-siteDirect career pageEst. 12–22 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Develop and optimize large-scale data ingestion and processing pipelines using SQL, Spark, and Python/Scala, ensuring performance and cost-efficiency.
- Design and implement data models with Dimension and Fact tables, including Slowly Changing Dimensions and incremental processing strategies.
- Build and maintain AWS serverless data engineering solutions (Glue, S3, Lambda, EMR) with robust error handling, logging, monitoring, and data quality checks.
Minimum Requirements
- 4–7 years of relevant Data Engineering experience.
- Strong proficiency in SQL and hands-on experience with Apache Spark and Python or Scala.
- Practical experience with AWS data engineering services, specifically AWS Glue and S3.
- Solid understanding of data modelling concepts (Dimension/Fact tables, grain, SCD types) and data engineering fundamentals like partitioning, incremental processing, schema evolution, and pipeline reliability.
Ideal Candidate Profile
- Experienced in designing and optimizing distributed data processing pipelines with a focus on scalability and reliability.
- Skilled in implementing complex data models and pipelines on AWS, including use of Spark and serverless architectures.
- Familiar with advanced tools and architectures such as Apache Iceberg, DBT, Airflow, and possibly Kafka for workflow orchestration and lakehouse data management.