Our client is looking for an early-career Data Engineer with strong fundamentals who wants to grow into MLOps and AI platform engineering. You’ll work alongside Data Scientists, Analysts, and business teams to build reliable pipelines and help take models from notebook to production.
What you’ll do
- Build and maintain scalable ETL/ELT pipelines, data lakes, and warehouses on platforms like Snowflake or Databricks
- Automate data ingestion, transformation, and orchestration, with data quality and observability built in
- Deploy and manage workloads on AWS (or Azure/GCP) using containers and Kubernetes
- Help operationalize ML and AI models: deploy them, monitor performance and drift, and keep them running reliably
- Contribute to code reviews, testing, CI/CD, and ongoing improvements to platform reliability and security
What you bring
- 1–3 years in data engineering, DataOps, or MLOps
- Strong Python and SQL skills, including query optimization
- Hands-on experience with ETL/ELT pipelines and a modern data platform (Snowflake, Databricks, or similar)
- Experience with a major cloud provider and Kubernetes or another container orchestration tool
- Solid understanding of modern data architecture and Git-based development practices
- Bachelor’s degree in Computer Science, Engineering, or a related field
Bonus points for
- Deploying ML or LLM/GenAI applications in production
- MLflow, SageMaker, Kubeflow, or Databricks ML
- Airflow, Dagster, or Prefect
- Docker, Terraform/CloudFormation, and CI/CD pipelines
- Monitoring tools (Datadog, Grafana, CloudWatch) or streaming with Kafka
Junior Data Engineer in New York at Gotham Technology Group
This position is listed as full time and onsite. It was posted yesterday.