Data Engineer

Data Engineer

Department: IT – Enterprise Data & Analytics

Location: Hybrid in New York, NY (4 days onsite per week, 1 day remote)

Employment Type: Full-Time

Experience Level: 1–3 Years

About the Role

Our client is seeking an early-career Data Engineer with 1–3 years of experience to join their Enterprise Data & Analytics team. This is an opportunity for a technically motivated individual to build on their data engineering foundation while gaining exposure to cloud infrastructure, DataOps, MLOps, and production AI applications.

The ideal candidate will have hands-on experience developing data pipelines, working with modern cloud-based data platforms, and supporting scalable data solutions. You will collaborate with Data Scientists, Data Analysts, and business stakeholders to build reliable data infrastructure and help deploy, maintain, and operationalize AI/ML applications in production environments.

Key Responsibilities

Data Engineering & Data Platform Development

• 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.
• Work with modern data platforms such as Snowflake, Databricks, or similar technologies.
• Implement data quality checks and support pipeline reliability, governance, and monitoring.

Cloud & Platform Engineering

• Deploy and support data workloads within AWS or other cloud environments, including Azure and GCP.
• Support containerized application deployments and cloud-based infrastructure.
• Assist with Kubernetes-based deployments and related cloud services.
• Monitor platform performance, troubleshoot issues, and support production operations.

AI/ML Operations & Production Support

• Support the deployment, monitoring, and maintenance of AI/ML applications and services in production.
• Collaborate with Data Scientists to operationalize machine learning models and AI-driven solutions.
• Gain exposure to MLOps tools and practices, including MLflow, SageMaker, Kubeflow, or equivalent platforms.
• Monitor application health, model performance, data quality, and overall system reliability.

Collaboration & Continuous Improvement

• Work with cross-functional technical and business teams to understand requirements and deliver data solutions.
• Participate in code reviews, testing, technical documentation, and CI/CD processes.
• Contribute to improving platform reliability, security, scalability, 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, or a related technical role.
• Strong proficiency in Python.
• Solid SQL skills and understanding of relational databases and query optimization.
• Hands-on experience developing and maintaining ETL/ELT data pipelines.
• Experience with Snowflake, Databricks, or another modern data platform.
• Exposure to AWS or another major cloud platform (Azure/GCP).
• Familiarity with Kubernetes or other container orchestration technologies.
• Understanding of data warehousing, data lakes, and modern data architectures.
• Experience using Git and following software development best practices.

Preferred Qualifications

• Exposure to deploying or supporting AI/ML applications in production environments.
• Familiarity with LLMs, Generative AI applications, inference endpoints, or AI service integrations.
• Exposure to MLOps tools such as MLflow, Kubeflow, SageMaker, or Databricks ML.
• Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
• Familiarity with Docker and containerized application deployments.
• Exposure to CI/CD pipelines and Infrastructure as Code tools such as Terraform or CloudFormation.
• Familiarity with monitoring tools such as Prometheus, Grafana, CloudWatch, or Datadog.
• Exposure to streaming technologies such as Kafka.

Ideal Candidate

An early-career Data Engineer with strong Python, SQL, and data pipeline development skills who is interested in expanding into cloud engineering, MLOps, and production AI application support. The ideal individual is technically curious, comfortable working across different areas of the data ecosystem, and eager to develop skills in emerging AI and cloud technologies.