AI Data Analyst

Overview

Hybrid

$50 - $51

Contract - W2

Contract - 5 Month(s)

Skills

3 to 5 years with Data Engineering in an enterprise environment

Strong Python

AI/ML pipelines and data flows

Job Details

Part-Time Data Engineer (San Francisco Bay Area Only)
Local Candidates Only | No Third-Party Agencies | No Visa Sponsorship Available

We are seeking a Part-Time Data Engineer to join a cutting-edge team focused on AI innovation in the pharmaceutical industry. This is a 20-hour-per-week contract position. Candidates can choose to work either 4 hours per day across 5 days or 5 hours per day across 4 days.

Please Note:
Only candidates currently residing in the San Francisco Bay Area will be considered.
We are not working with third-party agencies.
Visa sponsorship is not available for this role.
Candidates with more than 5 years of professional experience will not be considered.

About the Team: AI Emerging Tech & External Collaborations

You ll be joining a strategic innovation team driving the adoption of AI-enabled solutions and next-generation technologies to support our Pharma and DIA Partnering functions. Our focus is on building scalable data infrastructure, integrating external research capabilities, and enabling end-to-end AI workflows to accelerate decision-making and unlock real business value.

From collaborating with external research partners to shaping internal AI strategy, this is a unique opportunity to help define how data and AI transform a global organization.

Role Overview

As a Data Engineer, you will play a key role in architecting, building, and maintaining robust data systems that support machine learning and advanced analytics. You ll collaborate closely with data scientists, business stakeholders, and external collaborators to develop clean, accessible, and well-modeled datasets essential for AI innovation.

Key Responsibilities

Design & Build Scalable Data Pipelines: Develop and maintain pipelines for ingesting, transforming, and curating structured and unstructured data.
Data Profiling & Standardization: Analyze and align data sources, identify quality issues, and define models to support AI/ML use cases.
Data Product & API Development: Build reusable, secure data assets and APIs for analytics and machine learning workloads.
Infrastructure Architecture: Help design and implement scalable data platforms, including AWS-based data hubs.
AI/ML Integration: Ensure data solutions support downstream AI applications, including LLMs and AI agents.
Metadata & Governance: Embed data governance, lineage, and metadata practices into all solutions.
Monitoring Frameworks: Develop real-time data monitoring and alert systems.
Workflow Orchestration: Manage containerized workloads (e.g., Docker) and use orchestration tools like Amazon EKS or Google Agent Development Kit (ADK).
Continuous Improvement: Evaluate and refine data tools, processes, and platforms for performance and scalability.

Required Qualifications

Bachelor s or Master s degree in Computer Engineering, Data Science, or a related field
3 5 years of professional experience as a Data Engineer in an enterprise environment
Strong proficiency in Python
Experience with AI/ML data pipelines
Knowledge of data governance and metadata management best practices
Excellent written and verbal communication skills

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.