MLOps/Data Engineer

Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re actively looking for a hands-on Engineer to focus on MLOps/Data Engineering to build the data and ML platform that powers product decisions and production models.

As an MLOps/Data Engineer, you’ll report to the Data Science Manager and work closely with both our Data Science and Product teams. You’ll architect storage and compute, harden training/inference pipelines, and make our ML code, data workflows, and services reliable, reproducible, observable, and cost-efficient. You’ll also set best practices and help scale our platform as Nift grows.

Our Mission:

Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded.

We are a data-driven, cash-flow-positive company that has experienced 1,111% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by investors who supported Fitbit, Warby Parker, and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth here.

What you will do:

• Architecture storage: Design and implement our data storage strategy (warehouse, lake, transactional stores) with scalability, reliability, security, and cost in mind
• Pipelines ETL: Build and maintain robust data pipelines (batch/stream), including orchestration, testing, documentation, and SLAs
• ML platform: Productionize training and inference (batch/real-time), establish CI/CD for models, data/versioning practices, and model governance
• Feature model lifecycle: Centralize feature generation (e.g., feature store patterns), manage model registry/metadata, and streamline deployment workflows
• Observability quality: Implement monitoring for data quality, drift, model performance/latency, and pipeline health with clear alerting and dashboards
• Reliability cost control: Optimize compute/storage (e.g., spot, autoscaling, lifecycle policies) and reduce pipeline fragility
• Engineering excellence: Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility
• Cross-functional collaboration: Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance
• Roadmap standards: Drive the technical vision for ML/data platform capabilities and establish architectural patterns that become team standards

What you need:

• Experience: 5+ years in data engineering/MLOps or related fields, including ownership of data/ML infrastructure for large-scale systems
• Software engineering strength: Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds
• Cloud containers: Production experience on AWS, Docker + Kubernetes (EKS/ECS or equivalent)
• IaC: Terraform or CloudFormation for managed, reviewable environments
• Data engineering: Expert SQL, data modeling, schema design, modern orchestration (Airflow/Step Functions) and ETL tools
• ML tooling: MLflow/SageMaker (or similar) with a track record of production ML pipelines
• Warehouses lakes: Databricks, Redshift and lake formats (Parquet)
• Monitoring/observability: Data/ML monitoring (quality, drift, performance) and pipeline alerting
• Collaboration: Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup
• PySpark/Glue/Dask/Kafka: Experience with large-scale batch/stream processing
• Analytics platforms: Experience integrating 3rd party data
• Model serving patterns: Familiarity with real-time endpoints, batch scoring, and feature stores
• Governance security: Exposure to model governance/compliance and secure ML operations
• Mission-oriented: Proactive and self-driven with a strong sense of initiative; takes ownership, goes beyond expectations, and does what’s needed to get the job done

What you get:

• Competitive compensation, comprehensive benefits (401K, Medical/Dental/Vision), and we offer all full-time employees the potential to hold company equity
• Flexible remote work
• Unlimited Responsible PTO
• Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success