AWS Data Engineer - Integration & Modelling

Description

Key Responsibilities

• Design and implement data ingestion, transformation, and storage pipelines using AWS services such as Glue, Lambda, EMR, Redshift, and S3.
• Develop and optimize ETL/ELT workflows to support analytics, data science, and reporting requirements.
• Collaborate with data scientists, analysts, and business teams to understand data needs and ensure reliable data delivery.
• Build and maintain data lake and data warehouse architectures on AWS.
• Work with both structured and unstructured data, ensuring high quality, consistency, and availability.
• Manage data security, governance, and compliance according to organizational standards.
• Implement data validation, quality checks, and monitoring frameworks for pipelines.
• Optimize performance and cost across storage, compute, and data processing layers.
• Leverage Infrastructure as Code (IaC) tools like Terraform or CloudFormation for environment setup and automation.
• Support DevOps and CI/CD practices for automated data pipeline deployments.
  

Required Skills & Qualifications

• Bachelors or Masters degree in Computer Science, Information Systems, Data Engineering, or a related field.
• 58 years of professional experience in data engineering, ETL development, or cloud data solutions.
  

Hands-on Expertise In AWS Data Services Such As

• AWS Glue, S3, Lambda, Redshift, EMR, Athena, Step Functions, and Kinesis.
• Strong proficiency in SQL and Python for data processing and automation.
• Solid understanding of data modeling (OLTP and OLAP), data warehousing concepts, and performance tuning.
• Experience with ETL tools (AWS Glue, Talend, Informatica, dbt, or similar).
• Familiarity with big data technologies such as Spark, Hadoop, or PySpark.
• Knowledge of version control (Git) and CI/CD pipelines for data projects.
• Strong understanding of data security, encryption, and IAM policies in AWS.
  

Preferred Skills

• Experience with streaming data solutions (Kafka, Kinesis Data Streams, or AWS MSK).
• Familiarity with modern data stack tools like dbt, Airflow, Snowflake, or Databricks.
• Exposure to MLOps or data science pipeline integration.
• Knowledge of API-based data integration and RESTful services.
• AWS certification such as :
• AWS Certified Data Analytics Specialty or AWS Certified Solutions Architect Skills :
• Strong problem-solving and analytical abilities.
• Excellent communication and collaboration skills.
• Ability to work independently and deliver in agile environments.
• Detail-oriented with a focus on data quality and reliability.