分析工程师负责人 - 数据建模与质量
Lead Analytics Engineer - Data Modeling & Quality
Arcadia致力于为所有人带来更快乐、更健康的日子。我们相信,有一个更好的医疗世界——一个由数据驱动的世界。我们的平台将复杂多样的数据转化为统一的健康基础,帮助组织提供更好的护理,提高收入并降低成本。
我们是一支充满激情的团队,致力于让医疗更加可持续,我们正在寻找有热情的人才来帮助我们实现这一目标。
更多详情,请访问arcadia.io。
为什么这个职位对Arcadia很重要
Arcadia的数据平台为全国的健康计划、ACO和医疗机构提供人口健康分析。作为首席分析工程师——数据建模与质量,你将处于数据质量责任和分析数据建模的交汇点。你将负责SQL和DBT层,将原始的临床和理赔数据转换为可信的、生产级的数据集,同时还是这些模型所产生数据的质量权威。
这是一个混合角色——比传统的数据健康专业人员拥有更深的SQL和DBT专业知识,但比数据工程角色更具分析性和模型导向性。你更关注数据本身的逻辑、结构和可信度,而不是管道基础设施。
什么是成功的表现
3个月内
- 独立地诊断和解决数据管道质量问题
- 至少编写一个新的DBT模型或重构一个现有的模型以满足当前建模标准
- 了解从数据进入系统到银层和金层的整个流程,并能够追踪数据质量问题到其根源层
6个月内
- 与客户和跨职能合作伙伴(数据工程、客户成功)建立牢固的工作关系
- 深入熟悉Arcadia的完整数据栈——从数据进入系统到银层、金层以及下游消费者
- 推动至少一个改进项目,无论是技术性的(如模型重构、新的数据质量框架)还是流程性的(如推广指南、问题诊断流程)
12个月内
- 在部门内被认可为领导者——同事和利益相关者会主动寻求你在数据建模和质量方面的专业知识
- 能够在该职位的全部范围内独立工作,只需最少的指导
- 完成两个或更多的改进项目并投入生产,对数据质量和运营效率产生可衡量的影响
关于Arcadia
Arcadia.io帮助全国各地的创新医疗机构和支付方进行转型
查看英文原文
Arcadia is dedicated to happier, healthier days for all. We believe that there is a better healthcare world – one powered by data. Our platform transforms complex, diverse data into a unified foundation for health, helping organizations deliver better care, boost revenue, and lower costs.
We’re a team of fiercely driven individuals committed to making healthcare more sustainable—and we’re looking for passionate people to help us get there.
For more information, visit arcadia.io.
Why This Role Is Important to Arcadia
Arcadia's data platform powers population health analytics for health plans, ACOs, and provider groups across the country. As a Lead Analytics Engineer — Data Modeling & Quality, you sit at the intersection of data quality ownership and analytical data modeling. You'll own the SQL and DBT layer that transforms raw clinical and claims data into trusted, production-grade datasets, while also serving as the quality authority for the data those models produce.
This is a hybrid role — deeper SQL and DBT expertise than a traditional Data Health Professional, with a more analytical and model-focused scope than a Data Engineering role. You're less focused on pipeline infrastructure and more on the logic, shape, and trustworthiness of the data itself.
What Success Looks Like
In 3 months
- Independently triage and resolve pipeline data quality issues
- Author at least one new DBT model or refactor an existing one to meet current modeling standards
- Understand the end-to-end pipeline from ingress through silver and gold, and be able to trace a data quality issue to its root layer
In 6 months
- Building strong working relationships with clients and cross-functional partners (Data Engineering, Customer Success)
- Deeply familiar with Arcadia's full data stack — from ingress through silver, gold, and downstream consumers
- Driving at least one improvement project forward, whether technical (e.g. model refactor, new DQ framework) or process-focused (e.g. promotion playbook, triage workflow)
In 12 months
- Recognized as a leader within the department — peers and stakeholders seek out your expertise on data modeling and quality
- Operating independently across the full scope of the role with minimal guidance
- Two or more improvement projects completed and in production, with measurable impact on data quality or operational efficiency
About Arcadia
Arcadia.io helps innovative providers and payers across the country transform healthcare to reduce cost while improving patient health. We do this by aggregating large amounts of disparate data, applying algorithms to identify opportunities to provide better patient care, and making those opportunities actionable by physicians at the point of care in near-real time. We are passionate about helping our customers drive meaningful outcomes. We are growing fast and have emerged as a market leader in the highly competitive population health management software market and have been recognized by industry analysts KLAS, IDC, Forrester, and Chilmark for our leadership. For a better sense of our brand and products, please explore our website.
Protect Yourself
If you have concerns about the authenticity of a job offer or recruitment-related communication claiming to be from Arcadia, we encourage you to verify by contacting us directly at (781) 202-3600 and select option 3. For more information, visit our website.
This position is responsible for following all Security policies and procedures in order to protect all PHI under Arcadia's custodianship as well as Arcadia Intellectual Properties. For any security-specific roles, the responsibilities would be further defined by the hiring manager.
What You'll Be Doing
DATA MODELING & DBT DEVELOPMENT
- Author, review, and maintain DBT models using Spark/Hudi from ingest through bronze and silver
- Help clients understand their data model, assumptions, and limitations through intentional validation
- Troubleshoot and fix issues, then write DBT tests to catch issues proactively
- Optimize SQL performance for slow-running jobs
- Partner with Data Engineering on table design, partition strategy, and incremental patterns
DATA QUALITY OWNERSHIP
- Triage and classify data quality alerts, distinguishing source-level issues from transform-layer failures
- Design and maintain volume monitors and DQ monitors (null rate, distribution, future-date checks)
- Author and apply clinical DQ rules (entity volume, field coverage, LOINC coverage, referential integrity) and claims validation rules across silver and gold layers
- Conduct quality reviews for connector promotions — evaluating silver entity coverage, validation rule pass rates, and bronze-to-silver transformation correctness
- Own the ticket queue for DQ, attribution, hierarchy, and customer-specific data quality issues, writing clear customer-facing findings
CROSS-FUNCTIONAL QUALITY COLLABORATION
- Lead data quality reviews during connector installation and promotion (UAT → PRD), including claims validation playbooks and null analysis
- Partner with Data Engineering on root-cause triage for errors, ingress anomalies, and silver table issues surfaced through data quality monitoring
- Coordinate with the Measure Implementation Team (MIT) when data quality issues affect quality measure scores
- Contribute to and enforce data modeling standards across teams
TECHNOLOGIES
- Data modeling: DBT-Spark, SQL, Claude
- Warehousing: Amazon Redshift, Apache Hudi, AWS Athena
- Data quality: volume/DQ monitors, DBT tests
- Orchestration: Argo Workflows, Airflow
- Source control: Git / GitHub, PR-based review workflows
- Observability: Grafana, Jira
- Healthcare data: Claims (plan/professional/pharmacy), EHR (clinical entities), MPI
What You'll Bring
Education:
- Bachelor's or Master's degree in Computer Science, Statistics, Business, Economics, or a related field
Experience:
- Advanced SQL: window functions, complex CTEs, aggregation patterns, performance tuning on columnar databases
- DBT: hands-on experience authoring models, tests, macros, and yml documentation; familiarity with incremental strategies
- Healthcare data literacy: working knowledge of claims data (professional, institutional, pharmacy), clinical data (EHR entities), and common quality dimensions (member months, coverage rates, null patterns)
- Data quality mindset: ability to differentiate source data issues from transform issues, design systematic validation checks, and communicate data quality findings clearly
Skills:
- Clear communicator — able to translate technical findings for clients and non-technical stakeholders
- Strong analytical judgment — you can look at a distribution and know when something is wrong
- Ability to manage several projects simultaneously, leveraging AI tooling to stay organized and efficient
- Genuine desire to learn and apply AI tools for operational efficiency
Would Love For You To Have
- Experience with Spark SQL and Hudi table format
- Familiarity with data quality monitoring tools
- Comfortable operating in an AI-first environment using Claude to build/verify various day-to-day workflows
- Exposure to population health analytics concepts: HEDIS measures, risk adjustment, value-based care metrics
- Python scripting for data investigation and automation
- Experience with Argo Workflows or similar orchestration platforms
- Healthcare data standards: ICD-10, CPT, NDC, LOINC, NPI
What You'll Get
- Work alongside a talented team on some of the most complex and rewarding challenges in healthcare data
- Flexible, fully remote work environment with the resources and support to do your best work
- Exposure to senior leaders
- Be on the front lines of AI adoption — use cutting-edge tools to accelerate your work and shape how the team operates in an AI-first environment
- Make a meaningful impact on healthcare data operations by improving the quality, reliability, and trustworthiness of data that drives patient care decisions
- Be a part of a mission driven company that is transforming the healthcare industry
- Become a member of the talented, energized, diverse and purpose-driven Arcadian Community