Data Architect/Machine Learning Engineer

Insight Global
Oakland, United States of America
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Oakland, United States of America

Tech stack

API
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Big Data
Cloud Computing
Cloud Engineering
Data Architecture
Data Systems
Python
Machine Learning
Azure
Software Engineering
SQL Databases
Systems Integration
Cloud Platform System
Snowflake
Containerization
PySpark
Geospatial Data Abstraction Library (GDAL)
Dask
Machine Learning Operations

Job description

We are seeking a Data Architect / Machine Learning Advisor to join the Wildfire Consequence Modeling team. This role is ideal for a technical leader who can bridge the gap between research and practical implementation by combining expertise in data architecture, machine learning, cloud solution design, and modern data technologies.

Requirements

The ideal candidate will possess deep experience with AWS and emerging cloud-native technologies, enabling them to design scalable, secure, and production-ready solutions. They will be comfortable collaborating with academically oriented researchers while bringing a strong software engineering and architectural mindset to model development, deployment, and operationalization., Proven experience architecting, developing, and deploying machine learning and data solutions within AWS cloud environments.

Deep understanding of modern cloud-native architectures, emerging technologies, and best practices for scalable AI/ML platforms.

Strong background in both solution architecture and hands-on machine learning engineering.

Ability to evaluate business requirements and design practical, scalable, and maintainable technical solutions.

Experience translating research and analytical concepts into production-grade applications and data products.

Strong consultative skills with the ability to advise leadership on technology strategy, architecture decisions, and implementation approaches.

Excellent communication and stakeholder management skills, with the ability to effectively partner with Product Managers, researchers, engineers, and leadership teams.

Strong software engineering foundation and experience building enterprise-scale data and machine learning systems., * Professional experience developing and designing machine learning technologies and systems

  • Strong Python skills, with experience building production grade data and ML solutions

  • Experience with PySpark for large scale data processing

  • Ability to both problem solve analytically and design scalable models and systems

  • Comfortable operating as a hybrid Data Archiect/Data Engineer; hands on development as needed

Looking for very strong communication skills, ability to effectively communicate to leadership teams and Product Managers.


Technical & Platform Experience

  • Cloud & Data Platforms:

o AWS ecosystem, including SageMaker, S3, Lambda, Glue

o And/or experience with Snowflake

o And/or Palantir Foundry

  • Architecture & Systems:

o Solution architecture for data and ML systems

o Model pipelines, APIs, and system integrations


Geospatial & Domain Expertise

  • Strong geospatial analytics experience using Python, including tools such as:

o rioxarray

o GDAL

o rasterio

o geopandas

o dask

  • SQL experience supporting geospatial or analytical workloads

  • Experience or strong interest in wildfire spread or consequence modeling


Nice to Have / Preferred

  • Experience in wildfire modeling, environmental modeling, or risk/consequence modeling

  • Background working in applied ML environments where research transitions into production

  • Experience mentoring or supporting academic researchers in applied engineering contexts, * Opportunity to balance an academically driven team with practical, solution oriented engineering

  • High impact work supporting wildfire risk and consequence analysis

  • Strong influence over system design and technical direction

  • Blend of data science depth and software engineering rigor

Experience with geospatial analytics, environmental modeling, or wildfire-related applications is highly preferred.

Utility, risk modeling, or wildfire domain experience is a plus.

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