Aws Data Engineer

Role & responsibilities

We are seeking a highly skilled AWS Data Engineer with 68 years of experience in designing, building, and optimizing scalable data pipelines and cloud-based data platforms. The ideal candidate will have deep expertise in AWS native services, data modeling, ETL/ELT processes, and hands-on experience in enabling analytics solutions. You will collaborate with cross-functional teams including data architects, analysts, and business stakeholders to deliver high-performance, secure, and reliable data solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using AWS services (Glue, EMR, Lambda, Step Functions, etc.).
• Build and optimize data lakes and data warehouses on AWS (S3, Redshift, Snowflake on AWS preferred).
• Work with structured and unstructured data sources to enable advanced analytics and reporting.
• Implement and manage data ingestion frameworks from multiple sources (RDBMS, APIs, streaming data, IoT, logs).
• Develop, optimize, and maintain SQL/NoSQL queries, stored procedures, and transformations.
• Ensure data quality, security, and governance in compliance with enterprise standards.
• Collaborate with Data Architects and Business Analysts to design data models supporting BI and ML use cases.
• Drive automation of repetitive processes to improve reliability and efficiency.
• Monitor and optimize performance, cost, and scalability of data solutions.
• Provide production support for data pipelines and troubleshoot issues.

Preferred candidate profile

• 68 years of professional experience as a Data Engineer with a focus on AWS cloud.
• Strong proficiency in AWS services:

• Storage & Compute:

  S3, EC2, Lambda, EMR

• Data Processing:

  Glue, Kinesis, Step Functions

• Data Warehousing:

  Redshift, Snowflake (preferred)

• Orchestration:

  Airflow, AWS Step Functions
• Advanced knowledge of SQL, Python, or PySpark for data engineering.
• Experience with data modeling (OLTP, OLAP, Star/Snowflake schemas).
• Hands-on experience with big data frameworks (Spark, Hadoop).
• Strong understanding of data governance, security, IAM, and encryption on AWS.
• Knowledge of CI/CD practices and version control (GitHub, CodePipeline).
• Experience with monitoring & logging tools (CloudWatch, Datadog, Grafana).