We are looking for an experienced AWS Data Engineer to join our data engineering team and play a key role in designing, developing, and maintaining scalable data solutions on the AWS cloud platform.

The ideal candidate should have strong hands-on experience in data engineering, AWS services, ETL/ELT pipelines, data warehousing, and big data technologies.

You will work closely with data architects, data scientists, analysts, product teams, and business stakeholders to build reliable and high-performing data platforms that support analytics, reporting, and data-driven decision-making.

Key Responsibilities :

- Design, develop, and maintain scalable and reliable data pipelines using AWS cloud services.

- Build and optimize ETL/ELT workflows for processing structured and unstructured data from multiple sources.

- Develop data ingestion, transformation, and integration solutions using appropriate AWS services.

- Work with services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Lambda, Kinesis, Athena, Step Functions, and CloudWatch.

- Design and implement data models, data warehouses, and data lakes to support business and analytical requirements.

- Develop efficient and reusable data transformation frameworks using Python, SQL, PySpark, or similar technologies.

- Ensure data quality, accuracy, consistency, security, and governance across data pipelines.

- Monitor pipeline performance, troubleshoot failures, and optimize data processing jobs for scalability and cost efficiency.

- Implement automation and best practices around CI/CD, testing, deployment, monitoring, and operational support.

- Collaborate with cross-functional teams to understand business requirements and translate them into effective technical solutions.

- Participate in architecture discussions and contribute to the continuous improvement of the organization's data platform.

- Ensure compliance with security, access control, and data governance standards.

Required Skills & Qualifications :

- 4 to 9 years of relevant experience in Data Engineering with strong hands-on experience in AWS.

- Strong proficiency in Python and SQL.

- Hands-on experience building and managing ETL/ELT data pipelines.

- Strong knowledge of AWS data services such as S3, Glue, Redshift, Athena, EMR, Lambda, Kinesis, or Step Functions.

- Experience with PySpark/Spark and distributed data processing.

- Good understanding of Data Warehousing, Data Lakes, Data Lakehouse concepts, and dimensional data modelling.

- Experience working with relational and/or NoSQL databases.

- Understanding of data pipeline orchestration tools such as Apache Airflow, AWS Step Functions, or similar platforms.

- Experience with cloud security, IAM, encryption, monitoring, and performance optimization.

- Exposure to Git, CI/CD, Docker, and DevOps practices is preferred.

- Strong analytical and problem-solving abilities with excellent communication skills.

Good to Have :

- Experience with Copilot Studio or Microsoft Copilot technologies.

- Exposure to developing or integrating AI-powered data solutions, conversational AI, or intelligent automation.

- Experience working with APIs, LLM-based applications, or AI/ML data pipelines.

- Knowledge of Microsoft technologies such as Power Platform, Power Automate, or Azure services will be an added advantage.

- AWS certifications such as AWS Certified Data Engineer, AWS Solutions Architect, or AWS Developer will be a plus.