Data Engineer

Job Summary:

The Data Engineer designs builds and maintains scalable data pipelines and infrastructure to support AI-driven healthcare SaaS applications. This role ensures data integrity security and compliance while enabling advanced analytics and machine learning capabilities. The Data Engineer collaborates with cross-functional teams to deliver reliable data solutions that improve clinical and operational outcomes within secure scalable and compliant cloud-native environments.

Key Responsibilities:

1. Data Pipeline Development & ETL/ELT Engineering

• Design build and optimize robust ETL/ELT pipelines using tools such as Apache Airflow Talend Informatica or dbt.
• Transform raw healthcare data into structured formats for analytics and AI/ML model consumption.
• Ensure data quality integrity and reliability throughout the pipeline lifecycle.

2. Cloud-Native Architecture & AI Technologies

• Develop and maintain data infrastructure on cloud platforms (AWS Azure GCP).
• Engineer scalable data solutions using Fabric.
• Support real-time and batch data processing for advanced analytics and machine learning workflows.

3. Healthcare Data Standards & Compliance

• Ensure solutions adhere to healthcare data standards (HIPAA HL7 FHIR) and regulations (GDPR CCPA).
• Implement secure and compliant data handling practices across systems.
• Stay current with healthcare regulations and data privacy requirements.

4. AI/ML Workflow Support

• Prepare and manage data for AI/ML model development deployment and monitoring.
• Support feature engineering model monitoring and real-time data streaming for AI/ML initiatives.
• Collaborate with AI/ML engineers and data scientists to enable seamless integration of models into production.

5. Database Management & Optimization

• Manage and optimize relational and NoSQL databases (e.g. PostgreSQL MongoDB Cassandra DynamoDB).
• Write complex SQL queries perform joins and tune database performance.
• Ensure scalability reliability and security of data storage solutions.

6. DataOps Automation & Continuous Improvement

• Implement DataOps practices and automation for data workflows using Airflow dbt and CI/CD pipelines.
• Support streaming and real-time data solutions with Apache Kafka Spark Streaming or Flink.
• Commit to continuous improvement and staying current with industry trends and best practices.

7. Collaboration & Communication

• Work closely with product engineering clinical and compliance teams to deliver integrated data solutions.
• Share knowledge and mentor team members on data engineering concepts and tools.
• Communicate effectively to ensure alignment with business and technical goals.

Required Qualifications:

Education & Experience Guidelines

• Bachelors Degree in computer science data science or other relevant field.
• 5-8 years of relevant work experience
• Experience with cloud platforms data pipeline tools and healthcare data standards
• Exposure to AI/ML workflows and real-time analytics is a plus
• Occasional travel may be required.

Other Preferred Knowledge Skills Abilities or Certifications:

• Cloud Certifications: AWS Data Analytics Azure Data Engineer Google Cloud Data Engineer
• Streaming & Real-Time Data: Apache Kafka Spark Streaming Flink
• DataOps & Automation: Airflow dbt CI/CD for data workflows
• Security & Compliance: HIPAA GDPR CCPA data encryption
• Advanced Databases: PostgreSQL MongoDB Cassandra DynamoDB
• AI/ML Support: Feature engineering model monitoring ML pipeline integration

Fortive 9 Behaviors by Level:

Influencing & Mentoring

Customer Obsessed: Champions a customer-focusedculture by anticipating evolving needs and shaping solutions that deliver long-term value.

Strategic: Drives organizational impact by using data to derive insights that inform near-term and mid-range goals

Innovation for Impact: Influences innovation through demonstrating bold thinking and experimentation in own work and coaching others to do the same.

Inspiring: Demonstrates purpose-driven impact through expertise and collaboration.

Builds Extraordinary Teams: Drives impact through collaboration and influence by fostering trust sharing expertise and aligning efforts across teams.

Courageous: Influences and leads by example through action and integritymoves quickly toward goals tackles challenges head-on and encourages open sharing of ideas without fear.

Delivers Results: Leads complex initiatives to successful completion with high standards precision and urgency.

Adaptable: Applies rigor and stays true to process while fostering adaptability within the team.

Lead with FBS: Embraces FBSand models lean principles by mentoring peers and influencing teams and going to Gembafor first-hand insights.