Johnson & Johnson Innovative Medicine
Sr Pr Eng Data Engineering
Johnson & Johnson Innovative Medicine, Cambridge, Massachusetts, us, 02140
Data Lake Engineer and Solution Architect, R&D Therapeutics Discovery
Location
Spring House, Pennsylvania, United States of America
Beerse, Belgium
Job Summary The Data Lake Engineer and Solution Architect is responsible for designing, optimizing, and operationalizing the data lake to serve high‑dimensional biology teams, including High‑Content Imaging, High‑Throughput Transcriptomics, High‑Throughput Proteomics, among others. The candidate will optimize data models for high‑dimensional biology data teams, make high‑dimensional data AI/ML ready, tune storage and query performance for large‑scale combined analyses across high‑dimensional modalities, and deliver a standardized API for programmatic access.
Responsibilities
Design scalable data models and optimize schemas for high‑dimensional biological data.
Architect and tune data lakes for performance and cost efficiency.
Develop standardized APIs and SDKs for secure, streamlined data access.
Collaborate with scientific teams and vendors to deliver platform capabilities.
Maintain documentation and train users on best practices.
Implement governance, security, and compliance frameworks.
Qualifications
Degree in Computer Science, Data Engineering, Bioinformatics, or related field; advanced degree (MS/PhD) preferred.
7+ years in data/platform engineering, including 3+ years with data lakes.
Experience with biological data (omics, imaging) and analytic workflows.
Hands‑on expertise with Snowflake, SQL at scale, and cloud platforms.
Strong programming and scripting skills (Python, SQL), and pipeline orchestration tools.
Proven ability to design APIs and communicate technical trade‑offs effectively.
Core Expertise
Data modeling and schema optimization.
Performance tuning for data lakes and queries.
API development and secure data access.
Governance, lineage, and metadata management.
Cloud‑based data platforms and orchestration tools.
Programming in Python and SQL.
Preferred Qualifications
Familiarity with ML infrastructure and feature stores.
Advanced Snowflake optimization and cost‑control strategies.
Knowledge of data catalog tools and metadata standards.
Experience with containerization and CI/CD for data pipelines.
Background in omics or high‑dimensional imaging pipelines.
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Job Summary The Data Lake Engineer and Solution Architect is responsible for designing, optimizing, and operationalizing the data lake to serve high‑dimensional biology teams, including High‑Content Imaging, High‑Throughput Transcriptomics, High‑Throughput Proteomics, among others. The candidate will optimize data models for high‑dimensional biology data teams, make high‑dimensional data AI/ML ready, tune storage and query performance for large‑scale combined analyses across high‑dimensional modalities, and deliver a standardized API for programmatic access.
Responsibilities
Design scalable data models and optimize schemas for high‑dimensional biological data.
Architect and tune data lakes for performance and cost efficiency.
Develop standardized APIs and SDKs for secure, streamlined data access.
Collaborate with scientific teams and vendors to deliver platform capabilities.
Maintain documentation and train users on best practices.
Implement governance, security, and compliance frameworks.
Qualifications
Degree in Computer Science, Data Engineering, Bioinformatics, or related field; advanced degree (MS/PhD) preferred.
7+ years in data/platform engineering, including 3+ years with data lakes.
Experience with biological data (omics, imaging) and analytic workflows.
Hands‑on expertise with Snowflake, SQL at scale, and cloud platforms.
Strong programming and scripting skills (Python, SQL), and pipeline orchestration tools.
Proven ability to design APIs and communicate technical trade‑offs effectively.
Core Expertise
Data modeling and schema optimization.
Performance tuning for data lakes and queries.
API development and secure data access.
Governance, lineage, and metadata management.
Cloud‑based data platforms and orchestration tools.
Programming in Python and SQL.
Preferred Qualifications
Familiarity with ML infrastructure and feature stores.
Advanced Snowflake optimization and cost‑control strategies.
Knowledge of data catalog tools and metadata standards.
Experience with containerization and CI/CD for data pipelines.
Background in omics or high‑dimensional imaging pipelines.
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