Position Summary

We are seeking a highly experienced Senior DataOps Engineer to lead the design, governance, automation and operationalization of enterprise data platforms supporting advanced analytics and AI-enabled solutions.

Tech Stack required:

AWS

Git/GitOps

CI/CD

Amazon S3

AWS Glue

Amazon Athena

Amazon RDS for MySQL

Amazon EventBridge

Amazon Bedrock

AWS IAM

Secrets Manager and KMS

SQL; Python/PySpark. 

Knowledge of data lakes, data warehouses, data marts, data zones, knowledge bases and knowledge graphs.

Responsibilities:

Design the end-to-end DataOps landscape for the Salma MVP, integrating CRM data, product catalog, financing systems and the sales knowledge base through governed and reliable data flows.

Define the architecture and standards for CRM ingestion and staging, data and metadata models, dataset versioning, profiling, cleansing, lineage, quality controls and AI-ready datasets.

Design secure data-exposure patterns through API Gateway and integration interfaces, including access, identity, auditability and controlled exposure of data to Bedrock/LLM services and AI agents.

Establish GitOps and CI/CD standards for data workflows, environments, artifacts and scripts; define observability, resilience, performance and scalability requirements for data-processing workflows.

Guide the Proficient DataOps engineer, review implementations and coordinate with Backend Development on Lambda integrations, EventBridge messaging, CRM/MySQL access and agent tools.

Out of scope: development of Lambda application logic, modifications or configurations inside the CRM, front-end development and day-to-day infrastructure support.

Required Qualifications

Bachelor's degree in Computer Science, Software Engineering, Information Systems or related field.

5+ years of experience in Data Engineering, DataOps, DevOps or Cloud Data Architecture roles.

5+ years of hands-on experience designing data platforms on AWS.

Proven experience implementing enterprise-grade DataOps practices and CI/CD frameworks.

Strong experience defining data governance, quality and metadata management frameworks.

Demonstrated experience supporting analytics, AI and machine learning platforms.

Experience working in hybrid cloud and on-premises environments.