Technical Engineer

Location:

Hybrid-Hartford Area

The Technical Engineer Lead serves as the senior technical consultant and guide for the Advanced Analytics Engineering team within Enterprise Technology Solutions (ETS) at our client Insurance. This role is responsible for the management, upgrade, and support of the enterprise analytical platform serving 500 analytics users across all our client lines of business. The position combines deep technical expertise in multi-platform analytics infrastructure with team leadership, business stakeholder engagement, and strategic technology planning.

The role also encompasses organizational responsibilities including Disaster Recovery (DR) coordination, production pipeline sign‑off governance, and AI tool advocacy to modernize team workflows.

• Miniforge Python "Condaplus" - Linux, macOS, Windows, and AWS:
  Manage and maintain the Condaplus Python distribution across all enterprise platforms. This includes version upgrades and rollouts (desktop, Linux server, Windows server, macOS), coordinating security vulnerability remediation across devices, managing Conda virtual environments for application deployments, integrating with Nexus IQ firewall for automated security scanning of packages, and supporting AWS-based analytics environments including EC2 AMIs. Condaplus serves as the foundation for Python-based analytics, data science workflows, and internal tool development across the organization.
• SAS - Linux and Windows Across 9 Physical Servers:
  Oversee the SAS analytics platform including SAS 9.4 on RHEL 8 Linux servers and SAS Enterprise Guide on Windows. Responsibilities include planning and executing major SAS version upgrades (e.g., SAS 9.4M8, SAS Enterprise Guide 8.5), managing SAS/ACCESS client connectivity to enterprise databases (Teradata, DB2, Oracle) via ODBC configurations, coordinating user migration communications and timelines, troubleshooting SAS server performance and connectivity issues, and managing SAS web services and IIS integration for business‑facing applications.
  
  This includes maintaining SAS environments across multiple lines of business including Claims, BI, and PI.
• R Tools - Windows and 5 Physical Servers:
  Manage the R analytics ecosystem includes RStudio Server (transitioned from Commercial to Opensource, saving $66K annually), R version upgrades across platforms (Linux, Windows Desktop, AWS AMIs), Rtools for Windows package compilation, CRAN repository management and Nexus proxy configuration, and Git Lab Copilot integration with RStudio Server for version control workflows. Ensure R environments are consistent and accessible for the analytics user community.

• Analytics Platforms
  • SAS 9.4 (M8) – Enterprise analytics platform on RHEL 8 Linux (Claims, BI, PI environments)
  • SAS Enterprise Guide 8.5 – Windows desktop analytics client for 500 users
  • SAS/ACCESS Clients – Database connectivity modules for Teradata, DB2, Oracle, and other data sources
  • SAS Web Services / SAS Mid‑Tier – Web‑based SAS application delivery via IIS integration
  • SAS Stored Processes – Server‑side SAS programs invoked via web or application interfaces
  • RStudio Server (Opensource) – R development environment on Linux servers
  • R (multiple versions: 3.6.3, 4.4.3) – Statistical computing across Linux, Windows Desktop, and AWS AMIs
  • Rtools – Windows‑based R package compilation toolchain
  • Condaplus (Miniforge Python) – Custom Python distribution on Linux, Windows, macOS, and AWS
  • IBM SPSS – Statistical analysis software for business analytics users
  • PyCharm – Professional Python IDE for analytics development
  • KNIME Analytics Platform (Desktop) – Visual data science and analytics workflow tool
• Infrastructure & Operating Systems
  • RHEL 8 (Red Hat Enterprise Linux) – Primary Linux platform across 14 servers
  • Ubuntu 22.04 – AWS EC2 instances and GPU AMI environments
  • Windows Server – SAS and R platform hosting, IIS web services
  • GPFS (General Parallel File System) – High‑performance shared storage (/gpfs2/)
• AWS Cloud Services
  • AWS EC2 – Elastic compute instances for analytics workloads and ML environments
  • AWS S3 – Object storage for data lakes…