Senior Data Engineer – Machine Learning (W2 Only)

NLP PEOPLE; Berkeley Heights (NJ); On-site; USD 100,000 - 130,000; Full time

Job summary

NLP PEOPLE is looking for an experienced Data Engineer in Berkeley Heights, NJ. The role involves designing and maintaining scalable data pipelines, integrating machine learning models, and ensuring high data quality.

The ideal candidate has 5+ years of experience in Data Engineering and strong programming skills in Python. Proficiency in AWS services and experience with data platform technologies like Snowflake are essential. Apply today for a chance to contribute to data-driven solutions!

Qualifications

• 5+ years of experience in Data Engineering or related disciplines.
• Strong hands-on programming experience with Python.
• Solid understanding of machine learning workflows.

Responsibilities

• Design, build, and maintain scalable data pipelines.
• Develop feature pipelines for recommendation systems.
• Implement monitoring for data pipelines and ML services.

Skills

Python

SQL

Data Engineering

Machine Learning

AWS

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

AWS S3

Snowflake

Docker

Spark

Job description

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Clarkstech, is seeking the following. Apply via Dice today!

Job Location: Berkeley Heights, NJ or Alpharetta, GA

What You'll Do

• Design, build, and maintain scalable data pipelines processing large‑scale merchant datasets.
• Develop robust data models and analytical datasets to support downstream machine learning use cases.
• Implement batch and near real‑time data processing solutions.
• Ensure high standards for data quality, reliability, and performance across the platform.
• Optimize data workflows for scalability, maintainability, and cost efficiency.

Feature Engineering & Machine Learning Integration

• Build feature pipelines and model‑ready datasets used for recommendation systems and predictive models.
• Collaborate with data scientists to operationalize machine learning models.
• Develop and integrate model inference workflows into production systems.
• Support experimentation frameworks, model evaluation processes, and performance tracking.
• Translate business problems into scalable data and ML solutions.

Recommendation Systems Development

• Contribute to the design and implementation of recommendation engines using approaches such as nearest‑neighbor techniques, collaborative filtering, content‑based recommendations, embedding‑based methods, and machine‑learning‑driven recommendation models.
• Support model tuning, validation, and continuous improvement.

MLOps & Production Operations

• Build and maintain machine learning deployment pipelines.
• Automate model training, deployment, and promotion processes.
• Implement monitoring and observability for data pipelines and ML services.
• Manage model lifecycle activities, including versioning, retraining, and rollback strategies.
• Partner with platform teams to ensure production readiness and operational excellence.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or a related field.
• 5+ years of experience in Data Engineering or related disciplines.
• Strong hands‑on programming experience with Python.
• Experience building scalable data pipelines and data processing frameworks.
• Experience developing analytical datasets and performing feature engineering.
• Solid understanding of machine learning workflows and model integration.
• Experience supporting machine learning models in production environments.
• Strong SQL skills and experience working with large datasets.
• Experience designing data models for analytics and machine learning applications.

AWS Experience (Required)

• Amazon S3
• AWS Glue
• AWS SageMaker
• AWS ECS and/or Fargate
• AWS IAM
• AWS CloudWatch
• Event‑driven architectures and orchestration patterns
• Experience deploying and operating data and ML workloads within AWS environments.

Data Platform Experience

• Snowflake
• Data warehouse concepts
• Data lake architectures
• Metadata management and governance practices
• Performance optimization techniques

Preferred Qualifications

• Experience building recommendation systems in production environments.
• Familiarity with MLOps principles and frameworks.
• Experience with CI/CD pipelines for machine learning deployments.
• Knowledge of containerization technologies such as Docker.
• Exposure to orchestration tools such as Airflow or similar workflow platforms.
• Experience with distributed processing technologies such as Spark.

Nice to Have

• Experience with Generative AI, Agentic AI, or Large Language Model (LLM) applications.
• Familiarity with retrieval‑augmented generation (RAG) architectures.
• Experience integrating AI agents into analytical workflows.
• Knowledge of vector databases and semantic search techniques.

What Success Looks Like

• Build reliable, scalable data pipelines that transform raw merchant data into trusted analytical assets.
• Create feature engineering workflows that accelerate machine learning development.
• Operationalize recommendation models that improve business outcomes and customer experiences.
• Contribute across the full lifecycle of data and machine learning systems—from ingestion through production inference.
• Collaborate effectively within a pod structure, bringing strengths in either data engineering, machine learning, or both.

Ideal Candidate Profile

• Data Engineers who can move beyond traditional pipeline development and help build intelligent systems that generate insights, power recommendations, and drive data‑informed decision making.
• Someone who thrives in environments where data engineering, machine learning, and production operations converge, and enjoys solving complex problems using modern cloud‑native technologies.