Senior Data Platform Engineer (Observability)

Responsibilities

• Own the observability and search experience for the data platform, including the maintenance and evolution.
• Improve meta quality, lineage, and platform reliability to help users discover trusted data and make informed decisions with confidence.
• Implement and operate orchestration and pipeline management solutions using modern data stack tools (e.g., Dagster, Astronomer / Airflow), with a focus on reliability, observability, and scalability.
• Embed data lifecycle management into platform design, including retention, archival, and deletion controls aligned with regulatory obligations and client agreements.
• Ensure data ingestion and storage architectures are compliant with healthcare regulatory requirements (e.g., HIPAA), including secure handling of PHI, audit logging, and encryption in transit and at rest.
• Drive performance, reliability, and cost optimization across the data platform.
• Build and maintain scalable Terraform modules and pipelines that provision and manage Azure infrastructure supporting both client-specific and reusable firmwide analytics, alongside Snowflake, dbt, and Python components of the stack.
• Translate business, product, and client requirements into executable engineering plans, balancing near-term delivery needs with longer-term investments and collaborating closely with our Product Engineering, Analytics Delivery, and Security teams to align platform capabilities with firm priorities.
• Support the continued development of data and analytics culture across Chartis through knowledge share, cross-functional training, and mentorship.
• Mentor junior engineers and contribute to firmwide engineering standards around code quality, documentation, testing, and operational excellence.

Requirements

• 7+ years of experience in platform or data engineering roles, with a substantial portion focused on cloud infrastructure.
• Hands-on experience with data catalog and metadata management platforms such as DataHub, Apache Atlas, or similar tools.
• Strong understanding of data observability, metadata management, data lineage, and data governance concepts.
• Experience with cloud-based data warehouses (Snowflake or Databricks) and with orchestration tools (Azure Data Factory, Dagster, etc.).
• Proficiency with Python.
• Experience with data ingestion patterns, including batch processing and exposure to change data capture (CDC) concepts.
• Experience establishing engineering standards, CI/CD practices, and observability for data platforms.
• Experience building and scaling components of data platforms that support self-service analytics and multiple downstream consumers.
• Strong communication skills with the ability to translate between technical and non-technical stakeholders.
• Bachelor’s degree in a technology-related field of study (e.g. Computer Science, Health Informatics, Management Information Systems (MIS), Data Science, Analytics, etc.).

Skills

• Strong understanding of data observability, metadata management, data lineage, and data governance concepts.
• Experience with cloud-based data warehouses (Snowflake or Databricks) and with orchestration tools (Azure Data Factory, Dagster, etc.).
• Proficiency with Python.
• Experience with data ingestion patterns, including batch processing and exposure to change data capture (CDC) concepts.
• Experience establishing engineering standards, CI/CD practices, and observability for data platforms.
• Experience building and scaling components of data platforms that support self-service analytics and multiple downstream consumers.
• Strong communication skills with the ability to translate between technical and non-technical stakeholders.

Benefits

• Medical
• Dental
• Vision
• HSA
• FSA
• Disability Insurance
• Life Insurance
• 401(k) Match
• Paid Time Off
• Wellness Stipend
• Additional Voluntary Benefits

Pay

Salary range: $185,000 - $215,000, plus may be eligible for an annual discretionary bonus.

Schedule

Remote work is allowed.