AWS Data Engineer Profile Summary Data Engineer with 2–5 years of experience designing, building, and maintaining cloud‑native data platforms on AWS. Background in developing scalable ETL/ELT pipelines, integrating multiple data sources, and supporting enterprise analytics environments. Experienced in delivering reliable, secure, and high‑performance data solutions with a focus on automation, scalability, and data quality.
Key Capabilities Data Engineering - Cloud‑native data architectures on AWS, ETL/ELT pipeline development, data modeling, API and database integration, workflow orchestration.
AWS Services (Some) - Amazon S3, AWS Glue, Lambda, Redshift, Athena, EMR, Kinesis, IAM, CloudWatch, EventBridge.
Data Platforms & Tools - Apache Spark, Airflow, Snowflake (preferred), Databricks (preferred), Git/GitHub.
Programming DevOps - CI/CD fundamentals, Docker basics, infrastructure automation exposure, version control with Git.
Soft Skills - Strong analytical and problem‑solving skills.
- Clear communication with technical and business stakeholders.
- Collaborative team player with a client‑oriented mindset.
- Comfortable working in Agile environments.
- Continuous learning mindset and passion for AWS technologies.
Experience 2–5 years of hands‑on experience building, deploying, and supporting AWS‑based data platforms and production‑grade data pipelines.
Preferred Certifications - AWS Certified Data Engineer – Associate
- AWS Certified Solutions Architect – Associate
- AWS Certified Developer – Associate
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