Sr. AWS - Data Engineer

Role & responsibilities

We are seeking dynamic individuals to join our team as individual contributors, collaborating closely with stakeholders to drive impactful results.

Working hours - 5:30 pm to 1:30 am (Hybrid model)

Must have Skills*

1. 15 years of experience in design and delivery of Distributed Systems capable of handling petabytes of data in a distributed environment.

2. 10 years of experience in the development of Data Lakes with Data Ingestion from disparate data sources, including relational databases, flat files, APIs, and streaming data.

3. Experience in providing Design and development of Data Platforms and data ingestion from disparate data sources into the cloud.

4. Expertise in core AWS Services including AWS IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail, CloudWatch.

5. Proficiency in programming languages like Python and PySpark to ensure efficient data processing. preferably Python.

6. Architect and implement robust ETL pipelines using AWS Glue, Lamda, and step-functions defining data extraction methods, transformation logic, and data loading procedures across different data sources

7. Experience in the development of Event-Driven Distributed Systems in the Cloud using Serverless Architecture.

8. Ability to work with Infrastructure team for AWS service provisioning for databases, services, network design, IAM roles and AWS cluster.

9. 2-3 years of experience working with Document DB or MongoDB environment.

Nice to have Skills:

1. 10 years of experience in the development of Data Audit, Compliance and Retention standards for Data Governance, and automation of the governance processes.

2. Experience in data modelling with NoSQL Databases like Document DB.

3. Experience in using column-oriented data file format like Apache Parquet, and Apache Iceberg as the table format for analytical datasets.

4. Expertise in development of Retrieval-Augmented Generation (RAG) and Agentic Workflows for providing context to LLMs based on proprietary enterprise data.

5. Ability to develop re-ranking strategies using results from Index and Vector stores for LLMs to improve the quality of the output.

6. Knowledge of AWS AI Services like AWS Entity Resolution, AWS Comprehend.