Health Data Engineer - Mid

Responsibilities

• Data Pipeline Development: Architect and build scalable, reliable, and secure data pipelines to gather, process, and store healthcare data from multiple sources.
• Create and optimize ETL/ELT workflows, ensuring proper data extraction, transformation, and loading into analytics-ready databases or data warehouses.
• Design, implement, and manage cloud-based data engineering solutions using cloud platforms.
• Database Management & Optimization: Develop and maintain robust, scalable data storage solutions such as relational databases (SQL Server, PostgreSQL, MySQL) or NoSQL databases (MongoDB, Cassandra, DynamoDB).
• Tune database performance, troubleshoot issues, and implement optimizations to handle large-scale data sets efficiently.
• Ensure data reliability and integrity through schema design, normalization, and validation processes.
• Healthcare Data Integration & Analytics Support: Integrate data across healthcare applications and systems.
• Prepare clean, well-organized datasets for downstream analytics and machine learning projects to improve patient outcomes and healthcare workflows.
• Collaborate with data analysts and product teams to provide data solutions that support reporting, dashboards, and decision-making tools.
• Security, Compliance & Monitoring: Implement security measures to protect sensitive healthcare data and ensure compliance with healthcare regulations (e.g., HIPAA).
• Monitor data pipelines and infrastructure, addressing bottlenecks or failures to ensure system uptime and data accessibility.
• Proactively identify and address security vulnerabilities or inconsistencies in data systems.
• Collaboration and Documentation: Work closely with developers, DevOps engineers, product managers, and healthcare specialists to understand requirements, share insights, and align on project goals.
• Document data models, pipelines, API integrations, and best practices for reference and knowledge sharing.
• Contribute to the overall technical strategy for optimizing health data processing and usage.

Qualifications

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Minimum of 5 years of professional experience as a Data Engineer, Data Analyst, or a similar role, preferably with exposure to healthcare data projects or financial/banking systems.
• Proficiency with structured and unstructured data querying tools (SQL, PostgreSQL, or NoSQL databases like MongoDB).
• Strong knowledge of ETL/ELT processes, data warehousing, and analytics frameworks (e.g., Snowflake, Redshift, BigQuery, Databricks, or Apache Spark).
• Hands-on experience with data pipeline tools such as Apache Airflow, Talend, NiFi, or similar platforms.
• Familiarity with cloud platforms like AWS, Azure, or Google Cloud, including tools for data processing (e.g., AWS Glue, S3, Athena).
• Expertise in programming/scripting languages like Python, PySpark, Java, or Scala for data manipulation and automation tasks.
• Knowledge of containerization tools, including Docker and Kubernetes, for data system deployment.
• Familiarity with data visualization tools (e.g., Tableau, Power BI) and an understanding of analytics concepts.
• Understanding of healthcare data security and compliance requirements (e.g., HIPAA).
• Experience with encryption, data masking, and access controls to protect sensitive health information.
• Strong analytical and problem-solving capabilities, with the ability to work with large-scale datasets and maintain their integrity.
• Excellent communication and collaboration skills to work effectively with cross-functional teams and stakeholders.
• Ability to work in a fast-paced healthcare setting and adapt to evolving project requirements.
• Strong attention to detail, organization, and commitment to quality.