AI Data Engineer

As an AI Data Engineer, you will build and operate the data foundation that powers AI/ML and advanced analytics products and applications. You will own the end-to-end data operations lifecycle: sourcing and understanding data, designing scalable pipelines, enabling analytics and model development, and partnering with engineering and data science to productionize solutions. You will translate complex data into context for humans and AI applications, implement durable processes, and mentor junior team members.

Position Responsibilities: 

Data Acquisition & Domain Understanding

• Identify, evaluate, and obtain access to internal and external data sources with minimal guidance.
• Partner with subject matter experts to understand data definitions, quality, lineage, and business context.
• Document datasets using standard templates (metadata, assumptions, refresh cadence, known issues) and present findings to stakeholders.

Data Engineering & Platform Delivery

• Design, build, and maintain robust ETL/ELT pipelines to extract, transform, and stage data for AI/ML and BI consumption.
• Create and manage tables, schemas, and databases; implement data models that support analytics and machine learning use cases.
• Productionize pipelines and datasets with monitoring, alerting, data quality checks, and performance tuning.
• Collaborate with internal/external engineers and data scientists to integrate data science solutions into production systems.

Analytics, Measurement & Reporting

• Deliver ad-hoc analysis, business analytics, and reporting for projects of moderate scope/complexity.
• Define metrics and acquire data to measure solution effectiveness; communicate results and recommendations to peers and stakeholders.
• Build dashboards and reporting assets (e.g., Tableau/Power BI/Qlik) to support decision-making.

Process Design & Operational Excellence

• Help design scalable operating processes to implement analytics/AI insights, including:
• Clear roles and responsibilities across partners
• Defined workflows, system/data flows, and contingencies
• Checks-and-balances (validation, QA, approvals)
• KPI tracking and closed loop learning to continuously improve outcomes

Communication & Mentorship

• Communicate moderately complex technical and analytical topics to senior team members and business partners.
• Build a strong internal network and leverage senior peers to accelerate delivery.
• Mentor junior team members and contribute to team standards and best practices.

Required Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Math, or equivalent practical experience.
• 2+ years of relevant experience in data engineering, analytics engineering, or BI/analytics roles supporting AI/ML or advanced analytics.
• Strong programming skills in Python and/or Java; advanced SQL proficiency.
• Advanced experience with data transformation and modeling for analytics/BI and ML feature readiness.
• Strong understanding of relational databases and data modeling concepts.
• Working knowledge of distributed computing concepts/tools.
• Advanced experience with data visualization tools such as Tableau, Power BI, or Qlik.
• Analytics/ML/AI foundations

Preferred Qualifications:

• Comfortable with AI assisted with coding techniques to amplify productivity while retaining control over design (using Github copilot, Claude code, etc)
• Ability to explore and mine large structured and unstructured datasets using a systematic approach.
• Familiarity with statistics and common analytical techniques (e.g., regression, clustering, PCA, decision trees, survival analysis).
• Basic understanding of machine learning algorithms and familiarity with common AI/ML toolkits.

When you join our team:

• We’ll empower you to learn and grow the career you want. 
• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words. 
• As part of our global team, we’ll support you in shaping the future you want to see. 

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