AI Integration & Analytics Engineering Manager

Talent Octopusventures

Alpharetta (GA)

On-site

USD 115,000 - 192,000

Full time

14 days+

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Job summary

LexisNexis Risk Solutions is seeking a Manager – AI Integration & Agentic Analytics Engineering to lead the development of intelligent enterprise data products, AI-powered reporting, and modern integrations across business platforms.

You will head a team connecting enterprise source systems to our cloud data platform, champion AI Agents adoption to automate processes, and drive engineering efficiency with modern AI technologies. Strong leadership + hands-on data engineering is required.

Qualifications

  • Bachelor’s degree or higher in Computer Science, Engineering, Information Systems or related field.
  • Significant experience leading technical engineering teams on enterprise data platforms.
  • Experience delivering cloud-based data engineering solutions and enterprise reporting.

Responsibilities

  • Lead analytics and data engineering teams to deliver AI-powered enterprise data products and reporting solutions.
  • Oversee integrations across enterprise source systems and data platforms.
  • Collaborate with stakeholders across Finance, Operations, Product, and Security.

Skills

Leadership
Stakeholder management
SQL
Python
Data engineering
Enterprise reporting

Education

Bachelor’s degree in Computer Science or related field

Tools

Databricks
Azure Data Factory
Azure Data Lake Storage
Power BI
Azure OpenAI
Python
SQL
Spark
PySpark
REST APIs
Git
Azure DevOps
Terraform
Docker
Kubernetes

Job description

LexisNexis Risk Solutions is seeking a Manager – AI Integration & Agentic Analytics Engineering to lead the development of intelligent enterprise data products, AI-powered reporting, and modern integrations across business platforms.

You will head a team connecting enterprise source systems to our cloud data platform, champion AI Agents adoption to automate processes, and drive engineering efficiency with modern AI technologies. Strong leadership + hands-on data engineering is required.

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