Job Description

Role: AWS Data Engineer Exp.: 5+ years Location: Gurugram, Noida & Pune (Hybrid 3 days work from Office) Job Description : Candidate should Provide technical expertise in needs identification, data modeling, data movement, and translating business needs into technical solutions with adherence to established data guidelines and approaches from a business unit or project perspective. Good knowledge of conceptual, logical, and physical data models, the implementation of RDBMS, operational data store (ODS), data marts, and data lakes on target platforms (SQL/NoSQL). Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices. The candidate must be able to work independently and collaboratively Requirement: 5+ Years of experience as a Data Engineer Strong technical expertise in SQL is a must Strong knowledge of joins and common table expressions (CTEs) Strong experience with Python Experience in Data brick, Pyspark Strong expertise in ETL process and with various data model concepts Knowledge of star schema and snowflake schema Good to know about AWS services such as S3, Athena, Glue, EMR/Spark with a major emphasis on S3 and Glue Experience with Big Data Tools and technologies Key Skills: Good Understanding of data structures and data analysis using SQL or Python Knowledge of Insurance Domain is an addition. Knowledge of implementing ETL/ELT for data solutions end-to-end Understanding requirements, and data solutions (ingest, storage, integration, processing) Knowledge of analyzing data using SQL Conducting End to End verification and validation for the entire application Responsibilities : Understand and translate business needs into data models supporting long-term solutions. Perform reverse engineering of physical data models from databases and SQL scripts. Analyze data-related system integration challenges and propose appropriate solutions. Assist with and support setting the data architecture direction (including data movement approach, architecture/technology strategy, and any other data-related considerations to ensure business value)