The Role
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.
Summary Generated by Built In
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
Skills Required
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field
- 1–3 years of experience in Data Engineering, DataOps, MLOps, 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 of data warehousing, data lakes, and modern data architectures
- Experience with Git and software development best practices
- Experience deploying and supporting AI/ML applications in production
- Familiarity with LLM or 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, including Terraform or CloudFormation
- Exposure to monitoring tools such as Prometheus, Grafana, CloudWatch, or Datadog
- Experience with streaming technologies such as Kafka
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The Company
What We Do
Citizen Watch America represents Citizen Group’s sales and marketing across the United States, the Caribbean and Latin America. Its portfolio includes Citizen, Bulova, Frederique Constant, Alpina and Ateliers deMonaco, with the brands maintaining distinct identities and campaigns. The combined business aims to lead the affordable-luxury watch market, drawing on design, craftsmanship, innovation, technology, value and service to serve customers in those regions.
