Description

Experience: 3+ years of professional experience in Data Engineering roles, with at least 2 years focused on cloud-native data services.

Programming Expertise: Expert proficiency in one coding language like Python , Java or .NET.

Data Fundamentals:

• SQL Mastery: Solid expertise in writing complex and highly optimized SQL queries for relational databases and data warehouses.

• Data Modeling: Deep understanding of data structures, data modeling (e.g., dimensional modeling), and data access patterns.

• Diverse Data Stores: Experience working with a variety of databases, including relational (e.g., PostgreSQL, MySQL) , NoSQL (e.g., DynamoDB, CosmosDB) , and distributed file systems.

Cloud Proficiency (Practical Tooling):

• Proven hands-on experience in at least one major cloud platform, utilizing services critical to data engineering.

• AWS Examples: S3, RDS/Aurora, EMR, Glue, Athena, Redshift, Lambda.

• Azure Examples: Data Lake Storage, Azure SQL, CosmosDB, Azure Data Factory, Synapse.

Pipeline & Processing:

• Distributed Processing: Extensive experience with Big Data/distributed data processing frameworks like Apache Spark (PySpark) or Hadoop.

• ETL/ELT Frameworks: Strong experience building and maintaining data transformations using frameworks like PySpark and libraries like Pandas .

• Orchestration: Experience with modern workflow orchestration tools such as Apache Airflow or Azure Data Factory .

DevOps & Governance:

• Automation: Familiarity with building and using CI/CD pipelines for automated deployment.

• Infrastructure as Code (IaC): Experience with DevOps tools such as Git, Docker, and Terraform .

• System Design: Understanding of system design principles and experience in architecting robust, scalable, and secure data systems.