Senior Data Engineer

New

Vatica HealthHealthcare technology

United StatesFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 8–10 years of professional experience in data engineering or a related field.
Required Skills: AWSPythonSQLMachine LearningData engineeringRESTful APIsTerraform

Have 8–10 years of professional experience in data engineering or a related field.
Bring strong Python and advanced SQL skills.
Have experience delivering production data pipelines at scale.
Have hands-on experience with at least one workflow orchestration platform and big data processing frameworks.
Have experience developing and deploying production REST APIs, including integration, security, and automated testing.
Have experience with cloud platforms and data services.
Understand data warehousing, lakehouse architecture, and data modeling.
Have working knowledge of AI and machine learning workflows, LLM APIs, embeddings, and vector databases.
Have experience with Git, CI/CD, Docker, and infrastructure as code tools such as Terraform.
Preferred: experience building and operating solutions on AWS, and delivering production RAG or agent-based solutions.
Preferred: knowledge of data governance, cataloging, and lineage tools; cloud or data platform certifications; healthcare industry experience; or a related bachelor's or master's degree.

Design, build, and maintain batch and streaming pipelines for structured and unstructured data.
Develop ETL/ELT processes and optimize performance, cost, scalability, and reliability.
Build and operate cloud solutions using AWS services including EC2, ECS, Lambda, API Gateway, and SQS.
Develop and manage data workflows, including scheduling, dependencies, retries, alerts, and backfills.
Establish monitoring, logging, and observability for reliable production operations.
Design, develop, test, and maintain secure REST APIs and Python-based data services using FastAPI or Flask.
Build data foundations for machine learning and generative AI, including feature pipelines, datasets, embeddings, and retrieval-based solutions.
Integrate AI tools and LLM APIs into workflows for document processing, enrichment, classification, and summarization.
Implement data quality checks, validation, lineage, documentation, and security, privacy, and compliance standards.
Lead architecture and code reviews, mentor engineers, and communicate technical recommendations to stakeholders.

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