Member of Technical Staff (Data Engineering)
Solstice · New York City · $200,000–$300,000 (stated in the posting)
Why you should join us
Solstice is redefining how life sciences organizations commercialize their therapeutics. We are building a commercial engine that allows pharmaceutical marketers to launch campaigns at 100x the speed.
Rapid growth: over the past year, we have worked with some of the world's top life sciences manufacturers and more than 50 pharma brands.
Frontiers of technology: we are building applications and rapidly iterating at the frontiers of AI to expand what is possible in pharmaceutical marketing.
Top-tier investors: we have raised from investors including Transformation Capital, Twelve Below, Virtue, and the founders of Datavant, Commure, and Paradigm.
Building anything great requires commitment and dedication. We are looking for someone who wants to take real ownership of a difficult, consequential data problem.
About the role
We are hiring our first data engineer to build the data foundation for Solstice's media activation and measurement products. Our long-term goal is to close the loop between the content we create, the audiences who see it, and the outcomes it drives, then use that evidence to make the next campaign better.
Doing that requires reliable data across systems that were not designed to work together. Media partners and publishers produce daily delivery logs in different formats. Identifiers need to be reconciled back to healthcare professionals, campaigns, audiences, assets, and individual creative elements. Data arrives late, changes shape, contains duplicates, and includes off-list or unmatched records that cannot simply be dropped.
You will own this foundation end to end: the cloud warehouse, ingestion and transformation pipelines, identity and attribution mappings, data quality, observability, and the interfaces that make trusted data usable by our product, media, and AI teams. This is not a reporting or dashboard-only role. You will build the production systems that make measurement and learning possible.
What you'll be working on
Design, build, and operate the cloud data platform for Solstice's media products, from raw partner data through trusted, analysis-ready models.
Ingest daily event and delivery logs from DSPs, publishers, claims-data providers, client systems, and Solstice's own content platform.
Build durable mappings across vendor identifiers, NPIs, campaigns, audiences, assets, and atomic creative elements.
Handle the hard cases in third-party data: de-aggregation, off-list records, unmatched identities, duplicates, late-arriving data, schema drift, and backfills.
Create canonical data models that connect creative attributes and exposures to clicks, engagement, and longer-lag outcomes such as prescription or claims signals.
Build reliable batch and event-driven pipelines with clear lineage, idempotent processing, automated data-quality checks, and actionable alerting.
Make partner integrations repeatable, including APIs, secure file transfer, validation, reconciliation, and audit trails.
Provide clean datasets and services that support campaign measurement, experimentation, attribution, and the creative-to-performance feedback loop.
Own the reliability, security, and cost of the data platform as volume and the number of partners grow.
Work directly with media, product, AI, and customer teams to launch the first campaigns, investigate discrepancies, and turn real operating problems into durable platform capabilities.
What we're looking for
Must-haves
Strong Python and SQL, with experience building and operating production data systems.
Deep experience with ETL or ELT pipelines, data modeling, workflow orchestration, and a modern cloud warehouse or lakehouse.
Experience ingesting messy third-party data and reconciling records across systems with inconsistent identifiers and schemas.
Strong instincts for data correctness: validation, lineage, observability, backfills, deduplication, and failure recovery.
Experience designing systems that are reliable under growing volume, changing partner contracts, and imperfect upstream inputs.
Independent judgment and end-to-end ownership. You can turn an ambiguous business goal into a technical plan, ship it, and operate it.
Clear communication. You can explain data quality and measurement tradeoffs to technical and non-technical partners, and you are comfortable working directly with external vendors.
A high bar for craft, urgency, and follow-through.
Strongly preferred
Experience with GCP and BigQuery, or comparable depth with AWS, Azure, Snowflake, Databricks, or Redshift.
Experience with data transformation and orchestration tools such as dbt, Airflow, Dagster, or Prefect.
Experience in adtech, media delivery, measurement, attribution, or marketing data platforms.
Familiarity with healthcare or life sciences data, including NPI-keyed HCP data, claims, or prescription signals.
Experience building secure data systems for regulated, privacy-sensitive, or enterprise environments.
Experience preparing high-quality data for experimentation, analytics, machine learning, or AI systems.
Bonus points
Experience integrating with DSPs, endemic publishers, identity vendors, or claims-data providers.
Familiarity with creative-level or component-level measurement, incrementality, or experiment design.
Experience building a data platform from zero to one at an early-stage company.
Benefits
Health, dental, and vision insurance
Ground-floor equity opportunity
Unlimited PTO
401(k) Match
Visa sponsorship (O-1, H-1B, TN) available
Work in a high-velocity, high-impact environment
Competitive NYC compensation
This role offers a highly competitive NYC salary in the $200,000 to $300,000 range, calibrated to reflect experience, seniority, and the level of ownership you will take on as part of the early team. We benchmark against top-tier startups and established technology companies to ensure our compensation is fair and compelling, with flexibility at the upper end for exceptional candidates who can materially accelerate our data platform and media roadmap.