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AWS Data Engineer
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AWS Data Engineer (ETL Specialist)
Experience: 3-5 years (minimum 2+ years hands-on experience with AWS-native data engineering tools)
Location: Gigaplex, Airoli West
Role Overview: The AWS Data Engineer will design, develop, and manage scalable data pipelines and analytics infrastructure in a cloud-native environment. This engineer will be responsible for architecting complex ETL processes using AWS-managed services, optimizing data performance, and ensuring data quality, security, and observability across multiple systems. The ideal candidate has deep AWS knowledge, strong ETL design experience, and a solid grasp of modern data engineering practices.
Key Responsibilities:
- Design and implement end-to-end ETL workflows leveraging AWS services such as Glue, Lambda, Step Functions, EMR, Redshift, Kinesis, and S3.
- Develop and maintain data ingestion pipelines from structured, semi-structured, and streaming data sources.
- Design and maintain data lake and data warehouse solutions (S3, Redshift, Lake Formation).
- Build transformation logic with PySpark, SQL, or Python, ensuring performance and integrity.
- Orchestrate workflows using AWS Glue Workflows, Apache Airflow, or Step Functions.
- Implement data quality validations, monitoring frameworks, and automated alerts for pipeline health.
- Collaborate with data scientists, analysts, and application engineering teams to ensure data accessibility and alignment with analytics use cases.
- Ensure compliance with data governance and security frameworks (IAM, encryption, GDPR/HIPAA as applicable).
- Participate in data architecture reviews, contributing to design best practices for reliability and scalability.
- Document all data flows, transformations, and pipeline specifications for reproducibility and audits.
Required Technical Skills:
- Strong development background in Python and SQL.
- Expertise with AWS data services: Glue, Redshift, EMR, S3, RDS, Lambda, Kinesis, CloudWatch, and CloudFormation.
- Deep understanding of ETL/ELT design patterns, including incremental loads and change data capture (CDC).
- Familiarity with data modelling (Star/Snowflake schemas) and data lakehouse architectures.
- Experience working with large-scale or real-time datasets.
- Knowledge of data quality frameworks and data observability tools.
- Comfort with DevOps and CI/CD workflows using Git, CodePipeline, or Terraform.
- Advanced understanding of data security practices in AWS (IAM roles, encryption, network isolation).
Desired Skills:
- Hands-on experience with Snowflake, Databricks, or Athena.
- Familiarity with BI/analytics tools (QuickSight, Power BI, Tableau).
- AWS certifications such as AWS Certified Data Engineer – Associate or Data Analytics Specialty.
- Strong analytical and communication skills to translate business data needs into engineering solutions.
Educational Requirements:
- Master's or Bachelor’s degree in Computer Science, Data Engineering, or related technical field.
- AWS Data Engineering or Data Analytics certification preferred
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