Why This Role Is Important to Arcadia
Life sciences customers depend on Arcadia's real-world data to power drug development, safety surveillance, and outcomes research. As LS deal volume accelerates, the engineering foundation underneath delivery, i.e. quality, automation, data transformation evolution, and scale must keep pace.
This is a hybrid role at the intersection of data engineering, data analysis, and delivery operations. You'll refactor, scale, own, and operate an automated RWD data delivery pipeline via dbt/AWS architecture, serving as the primary technical point of contact for channel partners.
You write production-grade PySpark and dbt one day and may facilitate a data inquiry the next. You care deeply about both the correctness of the code and the clarity of the answer it produces. You're as comfortable in a GitHub PR as you are in a partner meeting.
This is a foundational engineering role in a growing LS organization. The right person will help build the team as the business scales.
What Success Looks Like
In 3 months
- Deep familiarity with the end-to-end LS pipeline-from ingestion through dbt transformation, de-identification, and delivery-including the current Snowflake-based scripts and what will replace them
- Ownership of the channel partner data inquiry queue; resolving standard requests independently by leveraging AI agents, closing out in writing and in accordance with SLAs
- First contribution to the delivery pipeline codebase: a new or refactored dbt model, a PySpark debugging fix, or a validated QC delivery configuration
- Thorough understanding of the monthly delivery cycle: Argo orchestration, Snowflake execution, manifest generation, Datavant/HealthVerity/IQVIA tokenization, and delivery QC
In 6 months
- Core delivery endpoint configurations migrated from manual Snowflake runbook to config-as-code; existing channel partners delivered with minimal manual script execution
- Contributing increasingly receptive metrics toward a data quality scorecard, tracking pipeline health, completeness, and refresh SLAs across all channel partners
- PHI de-identification compliance implementation process owned end-to-end, with clear documentation of rules applied
- Strong working partnerships established with platform engineering (Data Engineering, TechOps) with clear interfaces and shared standards
In 12 months
- Monthly delivery cycle runs automatically; manual Snowflake execution eliminated; delivery cycle time reduced
- Recognized internally as the technical authority on the LS data engineering architecture and delivery pipeline
- Test suites, acceptance criteria, and release documentation authored for all major pipeline changes
- Potentially beginning to mentor a junior team member as the LS delivery organization grows
Why This Role Is Important to Arcadia
Life sciences customers depend on Arcadia's real-world data to power drug development, safety surveillance, and outcomes research. As LS deal volume accelerates, the engineering foundation underneath delivery, i.e. quality, automation, data transformation evolution, and scale must keep pace.
This is a hybrid role at the intersection of data engineering, data analysis, and delivery operations. You'll refactor, scale, own, and operate an automated RWD data delivery pipeline via dbt/AWS architecture, serving as the primary technical point of contact for channel partners.
You write production-grade PySpark and dbt one day and may facilitate a data inquiry the next. You care deeply about both the correctness of the code and the clarity of the answer it produces. You're as comfortable in a GitHub PR as you are in a partner meeting.
This is a foundational engineering role in a growing LS organization. The right person will help build the team as the business scales.
What Success Looks Like
In 3 months
Deep familiarity with the end-to-end LS pipeline-from ingestion through dbt transformation, de-identification, and delivery-including the current Snowflake-based scripts and what will replace them
Ownership of the channel partner data inquiry queue; resolving standard requests independently by leveraging AI agents, closing out in writing and in accordance with SLAs
First contribution to the delivery pipeline codebase: a new or refactored dbt model, a PySpark debugging fix, or a validated QC delivery configuration
Thorough understanding of the monthly delivery cycle: Argo orchestration, Snowflake execution, manifest generation, Datavant/HealthVerity/IQVIA tokenization, and delivery QC
In 6 months
Core delivery endpoint configurations migrated from manual Snowflake runbook to config-as-code; existing channel partners delivered with minimal manual script execution
Contributing increasingly receptive metrics toward a data quality scorecard, tracking pipeline health, completeness, and refresh SLAs across all channel partners
PHI de-identification compliance implementation process owned end-to-end, with clear documentation of rules applied
Strong working partnerships established with platform engineering (Data Engineering, TechOps) with clear interfaces and shared standards
In 12 months
Monthly delivery cycle runs automatically; manual Snowflake execution eliminated; delivery cycle time reduced
Recognized internally as the technical authority on the LS data engineering architecture and delivery pipeline
Test suites, acceptance criteria, and release documentation authored for all major pipeline changes
Potentially beginning to mentor a junior team member as the LS delivery organization grows
What You'll Be Doing
RWD DATA PIPELINE ENGINEERING
- Author and maintain dbt models and PySpark transformation jobs, replacing ad-hoc Snowflake scripts with governed, version-controlled, tested code
- Design and implement delivery endpoint configurations as code-customer, delivery target (Snowflake, S3), cadence, cohort filters, incremental and full historical refresh methods
- Write production-grade Python and PySpark for data transformation, validation automation, and delivery pipeline components, including customer-specific data models and schema validation logic
- Configure and maintain AWS S3 delivery paths, Apache Iceberg table structures, and file staging patterns for partner data delivery
- Partner with platform engineering to build and extend Argo Workflows orchestration for automated delivery execution, eliminating the manual monthly Snowflake runbook
- Implement and maintain HIPAA de-identification compliance rules in pipeline code in accordance with ED certificates; coordinate certification updates when new data elements or rule changes affect certified products
- Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC
- Define and monitor delivery quality metrics: pipeline health, data completeness, referential integrity, refresh SLA tracking, and minimum volume thresholds; manage on-time delivery against a >=95% target
- Validate dbt model outputs and pipeline changes against expected schema and counts; define acceptance criteria and execute UAT before changes reach production or channel partners
- Own the channel partner data inquiry queue-triage, investigate, resolve, and communicate on data questions and discrepancies; you are the primary research contact for channel partners
- Conduct root cause analysis on anomalies in RWD, claims, and clinical feeds, distinguishing source-level issues from transform-layer failures, and communicate findings clearly in writing
- Build and maintain repeatable query libraries, data dictionaries, and end-to-end pipeline documentation in Confluence, reducing one-off analytical effort and improving institutional knowledge
- Build out a knowledge repository of learnings from all data inquiries, and partner with VP to provide a data FAQ for LS customers
- Contribute to data quality governance: build scorecards and trend analyses that equip leadership with evidence-based positions for engineering and product discussions
- Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence
- Manage engineering work through Jira: clear ticket authoring, acceptance criteria, dependency tracking, and proactive status communication
- Apply CI/CD practices via GitHub: branch management, PR-based review workflows, dbt model testing, and version control discipline-the version in production always matches what's in Git
- Partner closely with Arcadia's platform engineering team on pipeline architecture, table design, and data contracts
- Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughput
- Pipeline & transformation: dbt-Spark, PySpark, Python, SQL
- Orchestration: Argo Workflows, Kubernetes
- Storage: AWS S3, Apache Iceberg, AWS Glacier
- Warehousing: Snowflake (delivery target, Iceberg, analytics)
- Person De-identification and Tokenization Processes
- Source control & CI/CD: Git / GitHub, PR-based review workflows
- Workflow & observability: Jira, Confluence, etc.
- AI tooling: Claude, ChatGPT
DELIVERY OPERATIONS & DATA QUALITY
DATA INVESTIGATIONS & PARTNER SUPPORT
ENGINEERING PRACTICES & COLLABORATION
Technologies
What You'll Bring
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field (or equivalent professional experience)
- 5+ years of hands-on data engineering experience (production pipelines, dbt, Spark/PySpark, cloud data infrastructure) AND 5+ years of direct experience with life sciences RWD data (claims, EHR, clinical); these disciplines can overlap-5 years total is sufficient if you bring meaningful depth in both
- Production-grade SQL proficiency in Snowflake or a comparable columnar warehouse: complex joins, CTEs, window functions, incremental patterns – you write this fluently
- Python and/or PySpark for data transformation: you have written and debugged production Spark jobs, not just automation scripts
- dbt: hands-on experience authoring models, tests, macros, and yml documentation; familiarity with incremental strategies and model validation
- AWS S3: practical experience with file staging, delivery paths, bucket structure, and lifecycle management in a data engineering context
- HIPAA de-identification: working knowledge of Safe Harbor requirements and how they are applied in data pipelines before data leaves your custody
- SDLC fundamentals: you write requirements, author test plans, manage releases, and document your work – this is not new to you
- CI/CD and source control: Git/GitHub, PR-based review workflows, branching strategies
- External customer experience: you have led (or actively presented within) technical data discussions with partner analytics or science teams and can communicate complex data concepts clearly in writing and verbally
- Self-starter who operates independently in ambiguous, high-growth environments and a natural collaborator when the work calls for it
- Strong analytical judgment – you can look at a distribution and know when something is wrong
- Clear communicator – able to translate technical pipeline findings for partners and non-technical stakeholders
- Builder's mindset – you don't just answer the question in front of you, you eliminate the conditions that caused it
- Genuine curiosity – AI tooling and motivated to apply it to your own workflows, not just in principle
- Multi-tasking ability – manage multiple parallel workstreams, triage competing priorities, and communicate proactively on risk
Experience
Skills
Would Love For You To Have
- Experience with Argo Workflows or a comparable orchestration platform
- Familiarity with patient tokenization or record linkage technologies and how they integrate into RWD delivery workflows
- Experience with HIPAA de-identification certification process and implementation workflow
- Healthcare data standards: ICD-10, CPT, NDC, LOINC, NPI
- Experience working at a healthcare data vendor, RWD aggregator, or analytics platform company
- Comfortable operating in an AI-first environment, using Claude or similar tools to build, verify, and accelerate day-to-day workflows
- Demonstrated interest in people leadership-you'd be excited to mentor and eventually grow a small team as the LS organization scales
What You'll Get
- Be at the center of a high-stakes, high-impact engineering RWD delivery pipeline you help create will define how Arcadia delivers RWD to life science partners at scale
- Become the definitive internal expert on one of the most complex and valuable real-world healthcare datasets in the market, with the autonomy to shape how it is engineered, measured, and delivered
- 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
- Flexible, fully remote work environment, with resources and support to do your best work
- Exposure to senior leaders across the entire life science and corporate engineering teams
- A clear path to grow into a player/manager role as Arcadia's life sciences delivery team scales
- Become a member of the talented, energized, diverse, and purpose-driven Arcadian community
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.
