Senior Data Platform Engineer

Chartis

8d•$185,000 - $215,000•Remote

About The Position

Working within Chartis’ Data Platform & Infrastructure team, the Senior Data Platform Engineer is a hands-on individual contributor responsible for building, scaling, and operating the Data Platform infrastructure that underpins Chartis’ advisory work. This role sits at the intersection of cloud infrastructure, platform reliability, and delivery execution, with a mandate to enable reliable, scalable, and self-service data consumption across consulting engagements, internal analytics, and productized offerings. You will be responsible for designing, deploying, and maintaining data asset management pipelines utilizing Airflow or Dagster. You should be comfortable creating well-crafted DAGs that provide clear search, and discovery for data assets using tools like DataHub. Chartis’ Data Platform & Infrastructure team plays an exciting role within Chartis, helping to design, build, and deploy cutting-edge capabilities to aid our clients across the healthcare industry while growing the firm’s overall data and analytics capabilities. We’re looking for collaborative, creative problem solvers with a strong desire to materially improve the delivery of healthcare. You are a doer who is comfortable operating in small, early-stage teams where everyone is hands-on.

Requirements

7+ years of experience in platform or data engineering roles, with a substantial portion focused on cloud infrastructure.
Bachelor’s degree in a technology-related field of study (e.g. Computer Science, Health Informatics, Management Information Systems (MIS), Data Science, Analytics, etc.)
Deep hands-on experience with a modern data orchestration tool such as Airflow, Dagster, or similar tools.
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, GitHub Actions, etc.)
Working knowledge of 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.
Enthusiasm and a desire for continuous learning in a fast-paced, entrepreneurial environment.

Nice To Haves

Exposure to healthcare data or familiarity with core healthcare data concepts (e.g., claims, clinical, operational data) is preferred; deep domain expertise is not required.

Responsibilities

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.
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.
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.
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.
Drive performance, reliability, and cost optimization across the data platform.
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.