ETL Engineer

Product Development

Position Overview

We are seeking an expert-level ETL Engineer to design, develop, and optimize scalable, high-performance ETL/ELT pipelines for our US Healthcare Revenue Cycle Management (RCM) platform. This role is critical for enabling AI/ML, GenAI, LLM Ops, Agentic AI, and RPA workflows, supporting analytics, predictive modeling, and automation across RCM modules like Claims, Prior Authorization, Coding, Collections, Scheduling, and EDI.

The role requires deep expertise in data engineering, cloud-native ETL, Big Data pipelines, and compliance governance, ensuring reliable, secure, and production-ready data solutions.

Job Roles & Responsibilities

ETL / Data Pipeline Development:

• Design, build, and maintain robust ETL/ELT pipelines for structured, semi-structured, and unstructured data across multiple sources.
• Implement batch and real-time data pipelines to support AI/ML, analytics, RPA, and autonomous agent workflows.
• Ensure data quality, cleansing, transformation, and normalization for downstream applications.
• Optimize pipeline performance for high throughput, low latency, and cost efficiency.

Cloud Data Engineering:

• Develop cloud-native ETL solutions on AWS, Azure, and GCP using services such as AWS Glue, Data Pipeline, Azure Data Factory, Google Dataflow, and serverless functions (Lambda, Cloud Functions, Azure Functions).
• Integrate Big Data platforms (Hadoop, Spark, Snowflake, Redshift, BigQuery) into scalable data workflows.
• Build data pipelines enabling AI/ML model training, inference, and Agentic AI operations.

AI/ML & Agentic AI Enablement:

• Collaborate with Data Scientists and AI Engineers to supply clean, transformed, and compliant datasets for model development.
• Support GenAI, LLM Ops, and autonomous agent workflows with reliable data pipelines.
• Implement pipelines to integrate RPA outputs and AI predictions for automated decision-making.

MLOps / LLM Ops Collaboration:

• Ensure CI/CD integration for ETL/ELT pipelines and AI/ML workflows.
• Collaborate with DevOps, MLOps, and LLM Ops teams to enable smooth deployment of data workflows supporting autonomous AI.
• Track data lineage, auditability, and pipeline versioning for compliance and governance.

Data Governance & Compliance:

• Ensure data pipelines comply with HIPAA, SOC 2, GDPR, and internal policies.
• Implement PHI masking, encryption, and access controls for sensitive healthcare data.
• Maintain metadata, audit logs, and pipeline documentation for transparency and regulatory readiness.

Collaboration & Mentorship:

• Work closely with Data Architects, Data Scientists, AI Engineers, and RPA teams to understand pipeline requirements and optimize workflows.
• Mentor junior ETL engineers on best practices, pipeline design, and compliance standards.
• Collaborate across functions to ensure pipelines meet business and operational goals.

Candidate Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related fields.
• 5–10+ years of experience in ETL/ELT development, data engineering, or Big Data pipelines.
• Hands-on experience with cloud ETL platforms, Big Data tools, and modern AI/ML data requirements.
• Proven experience integrating pipelines for AI/ML, GenAI, LLM Ops, RPA, and autonomous agent workflows.
• Strong knowledge of HIPAA, SOC 2, GDPR, and healthcare compliance standards.

Technical Expertise:

• Programming: Python, SQL, R, Scala, Java
• ETL / Data Pipeline Tools: Apache Airflow, Apache NiFi, Talend, Informatica, AWS Glue, Azure Data Factory, Google Dataflow, dbt
• Big Data Platforms: Hadoop, Spark, Snowflake, Redshift, BigQuery, EMR
• Cloud Platforms: AWS, Azure, GCP (serverless functions, storage, pipelines)
• Data Integration: APIs, microservices, event-driven streaming (Kafka, Kinesis, Pub/Sub)
• RPA & AI Integration: UiPath, Automation Anywhere, Blue Prism (feeding AI models)
• CI/CD & DevOps: Git, Docker, Kubernetes, Jenkins, MLOps/LLMOps pipelines
• Analytics & Visualization: Tableau, Power BI, Looker (preferred)

  Skillset:

• Strong data engineering and analytical skills for handling complex, large-scale healthcare datasets.
• Ability to design scalable, automated, and compliant ETL/ELT pipelines.
• Knowledge of AI/ML, GenAI, LLM Ops, and Agentic AI requirements for data readiness.
• Excellent problem-solving, collaboration, and documentation skills.
• Compliance-first mindset with attention to data governance, security, and regulatory standards

Strategic Impact:

• Enable production-ready, compliant data pipelines for AI/ML, RPA, and autonomous agent workflows in Healthcare RCM.
• Support data-driven decision-making, predictive analytics, and intelligent automation.
• Ensure scalable, cost-efficient, and reliable ETL workflows that meet business and technical goals.
• Provide technical leadership for ETL best practices and cross-functional collaboration.

Kindly Note: At Credence, we uphold the highest standards of integrity in our recruitment process. We do not charge any fees at any stage of the hiring process, and we strictly prohibit any third parties, vendors, or individuals from soliciting money in exchange for job opportunities at Credence.

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