Job Description

Job Title: Principal: Data Architect
Experience: 15+ years
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Key Responsibilities:
- Strategic Data Leadership: Define and drive the data architecture and AI strategy in alignment with organizational goals, focusing on data-driven decision-making and improving operational efficiency.
- Architecture Design: Lead the design and implementation of scalable and efficient data architectures that support advanced analytics, reporting, and machine learning applications.
- Integration Solutions: Oversee the development of data integration strategies, enabling seamless data flow across various healthcare systems, including RCMs, EHRs, Claims, Policy Admin, Billing systems, and analytics platforms.
- Data Governance & Compliance: Establish and maintain data governance frameworks, ensuring compliance with regulations such as HIPAA, FedRAMP, and HITRUST, while promoting data integrity, security, and quality.
- Collaboration with Stakeholders: Partner with executive leadership, enterprise architects, data scientists, analysts, and IT teams to understand data needs, provide architectural guidance, and ensure successful project execution.
- Emerging Technology Assessment: Evaluate and recommend emerging technologies and tools that can enhance data architecture and analytics capabilities in healthcare.
- Performance Metrics: Establish key performance indicators (KPIs) and metrics to evaluate product success and drive continuous improvement based on user feedback and data analysis.
- Documentation and Standards: Develop comprehensive documentation of data architecture standards, best practices, and processes for internal teams.
- Mentorship and Team Development: Mentor and guide data architects and data engineers, fostering a culture of continuous learning and innovation within the data team.
Experience:
- 15+ years of experience in data architecture, with a minimum of 2+ years in the healthcare domain.
Technical Skills:
- Expertise in data engineering frameworks and tools such as Apache Spark, Apache Kafka, and Airflow.
- Proficiency in relational, NoSQL, and vector databases, including SQL Server, Oracle, Teradata, PostgreSQL, MongoDB, Cassandra, SingleStore, and Pinecone.
- Proficiency in data warehousing solutions like Snowflake, Amazon Redshift, Google BigQuery, and Databricks.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud, and their data services.
- Strong experience with ETL processes and tools such as Apache NiFi, Talend, Informatica, and Mulesoft.
- Experience with machine learning frameworks like Python, Keras, and TensorFlow.
- Expertise in data visualization tools such as Power BI and Tableau.
- Experience with generative AI, large and small language models, and retrieval-augmented generation (RAG) solutions such as GPT, Llama2, Claude 2, and Huggingface.
- Familiarity with healthcare data interoperability frameworks, including HL7 V2, HL7 V3, FHIR, and SNOMED, is a plus.
- Experience with microservices architecture, BFF, Spring, JDK, MERN or MEAN stack is a plus.
- Familiarity with big data technologies such as Hadoop and Spark is a plus.
Regulatory Knowledge:
- In-depth understanding of healthcare data regulations and standards, including compliance requirements.
Leadership Skills:
- Proven ability to lead cross-functional teams and drive data initiatives from conception to execution.
Responsible Use of AI:
- Familiarity with data privacy and ethical considerations in healthcare analytics.
Analytical Mindset:
- Strong analytical and problem-solving skills, with a focus on delivering actionable insights from data.
Communication Skills:
- Excellent verbal and written communication skills, capable of conveying complex data concepts to diverse audiences.
Qualifications:
- Engineering degree from a reputed university; a Master's degree is a plus.
- Advanced certifications in architecture, such as TOGAF or AWS/Azure/GCP Solutions Architect.
- Advanced certifications in data management, including CDMP, Cloud Data Architect, Cloud AI Solutions Architect, or Snowflake Architect.