Data Engineer

Overview

We are looking for a highly skilled Data Engineer with strong experience building AI-ready data pipelines, designing scalable cloud architectures, and implementing CI/CD over data and infrastructure workflows. This role sits at the intersection of data engineering, machine learning operations (MLOps), and cloud infrastructure, and will play a key role in turning raw data into production-grade AI systems.

Responsibilities

Data & AI Pipelines

• Design, build, and maintain end-to-end data pipelines supporting analytics and AI/ML use cases
• Develop feature pipelines and data transformations for model training, inference, and monitoring
• Implement batch and streaming pipelines using modern data stacks
• Ensure data quality, lineage, versioning, and observability across pipelines

AI / MLOps Enablement

• Build and operationalize model training and inference pipelines
• Integrate data pipelines with ML frameworks and orchestration tools
• Support model versioning, experiment tracking, and reproducible training
• Collaborate with data scientists and ML engineers to productionize models

Cloud & Architecture

• Design scalable, fault-tolerant cloud architectures for data and AI workloads
• Work with cloud services (AWS, GCP, or Azure) for storage, compute, orchestration, and networking
• Optimize cost, performance, and reliability of data infrastructure
• Make architectural decisions around data lakes, warehouses, feature stores, and serving layers

Infrastructure as Code & CI/CD

• Implement Infrastructure as Code (IaC) using tools like Terraform, Pulumi, or CloudFormation
• Build CI/CD pipelines for data workflows, ML pipelines, and infrastructure changes
• Automate testing, validation, and deployment of data and AI systems
• Enforce best practices around security, secrets management, and access control

Collaboration & Ownership

• Work cross-functionally with product, ML, and engineering teams
• Own systems end-to-end, from design through production and monitoring
• Document architectures, pipelines, and operational processes

Required Qualifications

• 4+ years of experience in data engineering or platform engineering
• Strong proficiency in Python (and/or Scala) for data and pipeline development
• Experience building AI/ML pipelines (training, inference, or feature engineering)
• Solid experience with cloud platforms (AWS, GCP, or Azure)
• Hands-on experience with CI/CD pipelines and Infrastructure as Code
• Strong understanding of data modeling, distributed systems, and pipeline orchestration

Preferred / Nice to Have

• Experience with orchestration tools (Airflow, Dagster, Prefect, Argo)
• Experience with data warehouses and lakes (Snowflake, BigQuery, Redshift, Delta/Iceberg)
• Familiarity with MLOps tools (MLflow, SageMaker, Vertex AI, Kubeflow)
• Experience with streaming systems (Kafka, Pub/Sub, Kinesis)
• Background in building production AI systems, not just experimentation

What Success Looks Like

• Reliable, scalable data pipelines powering AI and analytics use cases
• Clean, automated deployments of data and ML infrastructure
• Reduced time from data ingestion to model production
• Clear, well-documented architectures that scale with the business