Engagement Terms: Full-time (~40 hrs/week) | 6-month initial contract (extension possible)
Timezone Requirement: Strict overlap with U.S. working hours (EST–PST)
Availability: Immediate start or short notice / bench availability required
Schedule Constraint: Full-time only; no part-time or non-overlapping schedules

Role Summary

We are seeking a Senior Data Engineer to join a high-growth SaaS environment focused on building scalable data infrastructure and enabling ML/analytics workflows. The role is centered on designing and maintaining robust data platforms, supporting real-time and batch processing systems, and enabling data science and machine learning initiatives.

Technical Requirements (Recent 2–3 Years Hands-On Experience)

• Data Engineering: Apache Kafka (streaming), Apache Airflow (orchestration), advanced SQL, NoSQL databases
• Cloud & APIs: Strong AWS experience (preferred), API development experience
• Infrastructure: Docker, Kubernetes, Terraform / CloudFormation (IaC)
• CI/CD: Experience building and maintaining deployment pipelines
• Observability & Governance: Monitoring, logging, data quality, and governance frameworks

• Nice to have: ML pipelines, AWS SageMaker, OpenAI or LLM-based integrations

Key Responsibilities

• Design and implement scalable batch and streaming data pipelines
• Build and evolve data platform infrastructure supporting analytics and ML use cases
• Develop event-driven architectures using Kafka and secure API systems
• Collaborate closely with Data Science and ML teams to enable production-ready workflows

• Ensure data reliability, observability, governance, and system performance across platforms

Candidate Profile

• Proven expertise in Kafka, Airflow, and AWS-based architectures
• Experience building production-grade data platforms at scale
• Strong understanding of distributed systems and event-driven design
• Ability to work in fast-paced, product-driven environments