Senior Data and AI Specialist
New
J
JobgetherTelecommunications
The role is remote and is specifically based in Ontario, British Columbia, or Alberta, CanadaFull-TimeSenior
SalaryCAD $99,760–$124,700
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLBusiness IntelligenceTableauMicrosoft ExcelGoogle SheetsLookerLLM
Requirements
- 5+ years of professional experience developing technical data, analytics, automation, or related solutions.
- Extensive experience with SQL, relational databases, and complex data schemas.
- Strong proficiency with Google Sheets or Excel and experience with business intelligence platforms such as Tableau and/or Looker.
- Hands-on experience using Python, APIs, or workflow automation platforms such as Airtable, Coefficient, Zapier, or n8n.
- Experience building AI agents and applying LLM technologies (e.g., Claude, OpenAI, Gemini).
- Strong understanding of data management, governance, quality, and hygiene.
- Ability to break ambiguous business problems into structured, manageable components.
- Excellent written and verbal communication skills for synthesizing complex analysis into actionable insights.
- Willingness to travel occasionally for project or team meetings.
Responsibilities
- Architect, test, and deploy LLM-powered agents and AI workflows that automate complex, multi-step, and previously manual data investigations.
- Build Python-based automation, APIs, and workflow solutions that transform repetitive processes into scalable, reliable operational capabilities.
- Develop both individual productivity tools and multi-user workflows that improve human efficiency and enable business teams to self-serve insights.
- Perform advanced SQL analysis and root-cause investigations across complex telecommunications datasets to improve quality, reduce fraud, and optimize margins.
- Create dashboards and repeatable business intelligence solutions using tools such as Tableau, Looker, Python, Google Sheets, or Excel.
- Partner with cross-functional teams to translate business problems into clear data requirements, governance standards, and pipelines.
- Take end-to-end ownership of data products, from scoping and requirements gathering through documentation, development, and delivery.
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