Technical Life Sciences Consultant, Pharma (Business Translator)

Fractal Analytics•New York, NY

9d•$0 - $150,000•Onsite

About The Position

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner. We are seeking Technical Life-sciences Consultant driving AI led business transformations for Fortune 50 Life-sciences clients in New York and New Jersey, using enterprise‑scale data and platform architectures on AWS and Databricks. The role ensures end to end Client management, program management, growth and making clients successful by ensuring solutions are scalable, secure, cost‑efficient, and aligned with modern data engineering, analytics, and AI/ML best practices. They should partner with engineering teams, data product owners, and business stakeholders to establish architectural standards, design cloud‑native data platforms, and guide technical execution across complex programs.

Requirements

10+ years in AI, software development, data engineering, or data architecture
5+ years in life-sciences consulting
Driven Modernization initiatives for AI products with AI Foundations, governance, operational layers, context layers
Strong experience with Databricks, dbt Core/Cloud, Python, Spark, SQL, distributed compute paradigms (in‑memory, distributed, MPP), Data Vault 2.0 experience (including automate_dv)
Deep awareness with AWS services (S3, Glue, Redshift, EMR, DynamoDB, Lambda, Athena, Kinesis)
Life-sciences experience preferred

Nice To Haves

Experience with life-sciences datasets, migration and modernization
Understanding of operational layers (L1, L2, L3) and ontology/context layers

Responsibilities

Drive Senior client workshops, problem framing, solution framing
Driving e2e client advisory, opportunity creation, demand generation
Engage with business and technical teams to align architecture with requirements
Support RFP/RFI responses and architectural evaluations
Define and enforce data modelling, metadata, lineage, and quality standards
Implement CI/CD pipelines and monitoring frameworks
Ensure architectures support observability, reliability, and operational excellence
Experience architecting end‑to‑end solutions on AWS and Databricks
Define architectural standards, patterns, and best practices
Review and guide solution designs for scalability, performance, and security
Evaluate technology options and recommend long‑term architectural strategies
Ability to translate business and analytical requirements into scalable data models
Experience assessing current‑state data architectures and defining future‑state models
Experience designing scalable data models for analytical and operational workloads
Strong understanding of metadata, lineage, data quality, and governance frameworks
Familiarity with distributed compute paradigms and cloud‑native modeling patterns
Experience leading architecture across multi‑team, onsite/offshore delivery models
Executive presence to drive VP, Exec Director level meetings, steer-cos independently
Drive proactive thought leadership, technical visioning, workshops, in-person client success initiatives
Ability to translate complex analytical requirements into scalable architectures (data models, ETL pipelines, consumption layers)
Experience designing and deploying dashboards and self‑service analytics on relational and non‑relational databases
Strong grounding in CI/CD, DevOps, static code analysis, and test‑driven development
Experience with cloud migration patterns and modern data platform design
Experience defining data standards, metadata models, lineage, quality rules, and governance patterns
Experience implementing logging, monitoring, observability, and cost‑optimization frameworks
Experience driving adoption of enterprise data platforms and modern data practices