Senior Data Engineer – Machine Learning; W2

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Job Location:

Berkeley Heights, NJ or Alpharetta, GA

• 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.

• 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.

• 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.

• 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.

• 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.

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

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

• 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.

• 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.

• 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.

• 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.