Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines
• Develop data transformation workflows using dbt
• Create and optimize data models in Snowflake
• Process large-scale datasets using PySpark and Apache Spark
• Build and maintain AWS-based data solutions
• Implement data quality, monitoring, and governance practices
• Collaborate with analytics, BI, and data science teams
• Optimize data warehouse performance and cost efficiency

Requirements

✅ 4+ years of Data Engineering experience

✅ Strong proficiency in Python, PySpark, SQL, and Apache Spark

✅ Hands-on experience with Snowflake Data Cloud

✅ Experience developing and maintaining dbt models and transformations

✅ Strong knowledge of AWS services such as:

• AWS S3
• AWS Glue
• AWS Lambda
• AWS EMR
• AWS Redshift
• AWS Athena
• AWS IAM
• Amazon CloudWatch

✅ Experience building batch and streaming data pipelines

✅ Strong understanding of dimensional modeling, star schema, and snowflake schema

✅ Experience with workflow orchestration tools such as Airflow

✅ Knowledge of CI/CD pipelines and Git-based version control

✅ Experience with data quality, testing, and monitoring frameworks

✅ Excellent communication and stakeholder management skills

Preferred Qualifications

⭐ Experience with Kafka or real-time streaming platforms

⭐ Experience with Terraform or Infrastructure as Code (IaC)

⭐ AWS Certification (Data Analytics, Solutions Architect, or Developer)

⭐ Experience supporting analytics and machine learning workloads

⭐ Exposure to Agile/Scrum environments