Responsibilities
- Design and implement our data storage strategy (warehouse, lake, transactional stores) with scalability, reliability, security, and cost in mind
- Build and maintain robust data pipelines (batch/stream), including orchestration, testing, documentation, and SLAs
- Optimize compute/storage (e.g., spot, autoscaling, lifecycle policies) and reduce pipeline fragility
- Refactor research code into reusable components, enforce repo structure, testing, logging, and reproducibility
- Work with DS/Analytics/Engineers to turn prototypes into production systems, provide mentorship and technical guidance
- Drive the technical vision for data platform capabilities and establish architectural patterns that become team standards
Requirements
- 5+ years in data engineering, including ownership of data infrastructure for large-scale systems
- Strong coding, debugging, performance analysis, testing, and CI/CD discipline; reproducible builds
- Production experience on AWS, Docker + Kubernetes (EKS/ECS or equivalent)
- Terraform or CloudFormation for managed, reviewable environments
- Expert SQL, data modeling, schema design, modern orchestration (Airflow/Step Functions) and ETL tools
- Databricks (experience is a must), Spark, Redshift and Data lake formats (Parquet)
- Data monitoring (quality, drift, performance) and pipeline alerting
- Excellent communication, comfortable working with data scientists, analysts, and engineers in a fast-paced startup
Nice to Have
- Experience with large-scale batch/stream processing
- Experience integrating 3rd party data
- Experience building data products on medallion architectures
- 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
Benefits
- Competitive compensation
- 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
