Required Skills: aws

Job Description

Key Responsibilities

Data Engineering & Development

• Design, build, and maintain ETL/ELT pipelines using Snowflake, AWS Glue, Lambda, and other AWS services.

• Develop and optimize Snowflake data warehouses, schemas, views, and stored procedures.

• Implement data ingestion frameworks from structured and unstructured sources.

• Ensure performance optimization through Snowflake clustering, micro-partitioning, and query tuning.

Cloud Architecture & Integration

• Design end-to-end data architectures leveraging AWS services such as S3, Glue, Lambda, EC2, IAM, CloudWatch, SNS/SQS, and Kinesis.

• Integrate Snowflake with upstream and downstream systems using Snowpipe, Kafka, or AWS event-driven pipelines.

• Manage CI/CD pipelines for data engineering using tools like GitHub, GitLab, or AWS CodePipeline.

Data Governance & Quality

• Implement data validation, data quality checks, and automated monitoring.

• Ensure compliance with security policies including IAM roles, encryption, and access controls.

• Maintain documentation for data flows, architecture diagrams, and data dictionaries.

Collaboration

• Partner with data analysts, data scientists, and business stakeholders to support data needs.

• Work with DevOps teams to deploy scalable and cost-efficient data solutions on AWS.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.

• 3–7 years of experience in data engineering or cloud engineering.

• Strong proficiency in Snowflake (SQL, Snowflake architecture, performance tuning).

• Hands-on experience with AWS services:

  • S3, Glue, Lambda, IAM, CloudWatch, RDS/Redshift, Kinesis, EC2, Step Functions

• Advanced knowledge of SQL and experience with Python or PySpark.

• Experience with ETL/ELT design principles and distributed data processing.

• Knowledge of data modeling (dimensional, star/snowflake schema).

  Preferred Qualifications

• Snowflake certifications (SnowPro Core, SnowPro Advanced).

• AWS certifications (AWS Data Engineer Specialty, AWS Solutions Architect, or Developer).

• Experience with Apache Airflow or AWS Managed Airflow.

• Familiarity with dbt, Kafka, Terraform, or containerization (Docker/ECS/EKS).

• Experience with data lake architectures and Lakehouse concepts.