Analytics Engineer – Life Sciences Delivery Operations

Arcadia

Analytics Engineer managing data delivery operations using real-world data for life sciences. Authoring and maintaining data transformation jobs while collaborating with channel partners.

Posted 7/21/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $175,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data engineering with a strong focus on dbt, PySpark, and SQL for building production-grade data pipelines. Proficient in managing data delivery processes and ensuring compliance with HIPAA regulations while effectively communicating with external partners.

Highest-signal resume keywords

Data Engineering ExperienceProduction-Grade SQL ProficiencyDbt Model AuthoringPython/PySpark DevelopmentHIPAA Compliance Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringSQLDbtPythonPySparkAWS S3CI/CDSource ControlData TransformationSchema Validation

Soft Skills

CommunicationCollaborationProblem-SolvingSelf-StarterCustomer Engagement

Tools & Technologies

SnowflakeConfluenceGitGitHubClaude Code

Industry Keywords

Life Sciences RWDClaims DataEHRClinical DataSafe Harbor

Tech Stack

Tools & technologies

AWSCloudPySparkPythonSDLCSparkSQL

About the role

Key responsibilities & impact

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
Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC
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
Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence
Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughput

Requirements

What you’ll need

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.

Benefits

Comp & perks

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