Build and maintain scalable ETL/ELT pipelines, data warehouses, data lakes, and data platforms. Deploy and support cloud-based, containerized workloads using Kubernetes, monitor production systems, and improve data quality and reliability. Collaborate with data scientists to operationalize AI/ML applications, implement MLOps practices, monitor model performance and drift, and participate in testing, documentation, code reviews, and CI/CD processes.
The Data Engineer works closely with Data Scientists, Data Analysts, and Business stakeholders to develop reliable data solutions and operationalize machine learning and AI applications.
Key Responsibilities
Data Engineering
- Design, develop, and maintain scalable ETL/ELT pipelines for structured and semi-structured data.
- Build and optimize data warehouses, data lakes, and data models.
- Develop automated data ingestion, transformation, and orchestration workflows.
- Manage and optimize data platforms such as Snowflake, Databricks, or equivalent.
- Ensure data quality, reliability, governance, and observability across data pipelines.
Cloud & Platform Engineering
- Deploy and manage data workloads on AWS or similar cloud platforms (Azure/GCP).
- Implement infrastructure and application deployments using containerized environments.
- Support and maintain Kubernetes-based deployments and related cloud services.
- Monitor platform performance and troubleshoot production issues.
AI/ML Operations
- Support deployment, monitoring, and maintenance of AI/ML applications and services in production environments.
- Collaborate with Data Scientists to operationalize machine learning models and AI solutions.
- Implement basic MLOps practices using tools such as MLflow, SageMaker, Kubeflow, or equivalent platforms.
- Monitor AI application health, data drift, model performance, and system reliability.
Collaboration & Continuous Improvement
- Work closely with cross-functional teams to gather requirements and deliver scalable solutions.
- Participate in code reviews, documentation, testing, and CI/CD processes.
- Continuously improve platform reliability, security, and operational efficiency.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
- 1–3 years of experience in Data Engineering, DataOps, ML Ops, or a related role.
- Strong proficiency in Python.
- Strong experience with SQL and database optimization.
- Hands-on experience building and maintaining ETL/ELT pipelines.
- Experience with Snowflake, Databricks, or equivalent modern data platforms.
- Experience with AWS or similar cloud platforms.
- Experience with Kubernetes or equivalent container orchestration technologies.
- Understanding data warehousing, data lakes, and modern data architectures.
- Experience with Git and software development best practices.
Preferred Qualifications
- Experience deploying and supporting AI/ML applications in production.
- Familiarity with LLM/Generative AI application deployment, inference endpoints, or AI service integration.
- Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, or Databricks ML.
- Experience with orchestration tools such as Airflow, Dagster, or Prefect.
- Knowledge of Docker and containerized application deployment.
- Familiarity with CI/CD pipelines and Infrastructure as Code (Terraform, CloudFormation).
- Exposure to monitoring tools such as Prometheus, Grafana, CloudWatch, or Datadog.
- Experience with streaming technologies such as Kafka is a plus.
EEOE
Annual Salary range: $90,000.00 - $95,000.00
Citizen Watch America New York, New York, USA Office
New York, United States
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