Data Engineer

Title: Data Engineer (Early Career)

Duration: FTE/Permanent

Salary: 90-95k

Location: Onsite in NYC

Junior Data Engineer, Data Platform and MLOps

A growing data team is hiring an early-career Data Engineer to help build the foundation behind its analytics and AI work. If you have a couple of years of pipeline experience under your belt and want to get closer to how machine learning actually runs in production, this is a strong next step. You’ll sit alongside data scientists, analysts, and business partners, and your work will directly shape how models and AI tools get from the lab into everyday use.

A Typical Week Might Include

• Writing and improving ETL/ELT jobs that move both structured and semi-structured data into the warehouse and lake.
• Shaping data models and tuning performance on Snowflake, Databricks, or a similar platform.
• Automating how data comes in, gets cleaned up, and flows downstream.
• Adding checks and monitoring so the team can trust the data and catch problems early.
• Running and supporting workloads on AWS (Azure or GCP is fine too), including containerized services on Kubernetes.
• Jumping in to troubleshoot when something breaks in production.
• Helping data scientists package and deploy models, then keeping an eye on model performance, data drift, and service health afterward.
• Using MLOps tools like MLflow, SageMaker, or Kubeflow to keep the model lifecycle organized.
• Taking part in code reviews, testing, documentation, and CI/CD, and suggesting ways to make the platform more stable and secure.

Must Haves

• A degree in Computer Science, Engineering, Information Systems, or something close.
• Roughly 1 to 3 years working in data engineering, DataOps, MLOps, or a related area.
• Solid Python.
• Solid SQL, with experience making queries and databases run faster.
• Real experience building and supporting ETL/ELT pipelines.
• Time spent on Snowflake, Databricks, or a comparable data platform.
• Working experience in AWS or another major cloud.
• Exposure to Kubernetes or a similar container orchestration tool.
• A good grasp of how warehouses, lakes, and modern data architectures fit together.
• Day-to-day comfort with Git and clean development practices.

Nice to Haves

• You’ve helped deploy or support AI/ML applications in production.
• You’ve worked with LLMs or generative AI, such as inference endpoints or plugging AI services into applications.
• You’ve used ML lifecycle tools like MLflow, Kubeflow, SageMaker, or Databricks ML.
• You’ve built workflows in Airflow, Dagster, or Prefect.
• You know your way around Docker.
• You’ve touched CI/CD pipelines or Infrastructure as Code with Terraform or CloudFormation.
• You’ve used monitoring tools such as Prometheus, Grafana, CloudWatch, or Datadog.
• Bonus points for Kafka or other streaming experience.

The Right Fit

You’ve got the data engineering basics down and you’re genuinely excited to grow into MLOps, AI platform work, and keeping AI applications running smoothly in production.