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Описание вакансии
TL;DR
Lead Data Engineer (AI) (Data Engineering/AI): Assessing data sources, quality, integrations, and technical feasibility for AI, automation, and reporting use cases with an accent on ingestion, ETL/ELT, APIs, and platform recommendations. Focus on identifying implementation risks and dependencies, sizing follow-up build activities, and distinguishing quick pilots from deeper platform work.
Lead Data Engineer at Exadel
Тбилиси, Грузия, гибрид
Why Join Exadel
We’re an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks.
From AI platforms to digital transformation, we partner with enterprise leaders to build what’s next.
What powers it all? Our people are ambitious, collaborative, and constantly evolving.
What You’ll Do
- Review available data sources, access patterns, interfaces, and integration options
- Assess data availability, quality, completeness, and readiness for prioritized use cases
- Identify ingestion, ETL/ELT, API, and system integration requirements
- Validate whether proposed AI, automation, and reporting use cases are technically feasible
- Identify implementation risks, blockers, dependencies, and assumptions
- Support platform recommendation with implementation-level input
- Provide rough sizing for Phase 2 build activities
- Help distinguish what can be piloted quickly versus what requires deeper platform work
- Support technical documentation for final recommendations
Source:
Тбилиси, Грузия, гибрид
Why Join Exadel
We’re an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks.
From AI platforms to digital transformation, we partner with enterprise leaders to build what’s next.
What powers it all? Our people are ambitious, collaborative, and constantly evolving.
What You’ll Do
- Review available data sources, access patterns, interfaces, and integration options
- Assess data availability, quality, completeness, and readiness for prioritized use cases
- Identify ingestion, ETL/ELT, API, and system integration requirements
- Validate whether proposed AI, automation, and reporting use cases are technically feasible
- Identify implementation risks, blockers, dependencies, and assumptions
- Support platform recommendation with implementation-level input
- Provide rough sizing for Phase 2 build activities
- Help distinguish what can be piloted quickly versus what requires deeper platform work
- Support technical documentation for final recommendations
Source:
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