Key Responsibilities :
Data Architecture & Solution Development
- Translate business and functional requirements into robust, scalable data solutions aligned with overall architecture.
- Design and implement data integrations and data quality frameworks.
- Participate in full development lifecycle including design, implementation, testing, documentation, delivery, support, and maintenance.
Pipeline & API Development
Develop and maintain scalable data pipelines.Build new API integrations to support data flow and analytics requirements.Implement large-scale, high-volume, low-latency applications for efficient data processing.Big Data & ETL Development
Design, develop, and maintain ETL pipelines.Apply dimension modeling and data warehousing best practices.Write complex SQL queries for data transformation, extraction, and validation.Streaming & Advanced Processing
Build stream processing platforms using Kafka and Spark Streaming.Optimize data pipelines for performance and scalability.Independently deliver end-to-end projects with minimal supervision.Required Skills & Experience :
5+ years of development experience in big data using Spark.Strong programming skills in Python.Expertise in SQL and complex query writing.Experience with dimension modeling, data warehousing, and ETL pipelines.Knowledge of streaming platforms like Kafka and Spark Streaming.Nice to Have :
Experience with job orchestration frameworks like Airflow, Oozie, or Luigi.Familiarity with AWS services such as S3, EMR, RDS.Understanding of cloud data warehouses like Snowflake.Knowledge of SQL distribution engines like Presto, Druid.Familiarity with streaming frameworks like Flink.Exposure to NoSQL databases such as HBase or Cassandra.Skills Required
Python, Spark, Sql, Data Warehousing, Etl, Aws