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Senior/ Lead AI Engineer

HERE TechnologiesIkona lokalizacjiGlobalnie

Żródlo publikacji: JustJoin.it
Rodzaj zatrudnienia
Rodzaj zatrudnieniaPełny etat
Doświadczenie
DoświadczenieSenior
Dodano
Dodano22 lipca 2026
Wykryte przez nas
Wykryte przez nas22 lipca 2026
Zarobki
Zarobki15 000 - 29 000 PLN

We are seeking for an Engineer to support AI and data initiatives by building and enhancing the data and analytics foundations that enable customer insights and GenAI-driven use cases. This role focuses on developing scalable data ingestion, transformation, taxonomy, and analytics capabilities that support dashboards, KPIs, trend analysis, and AI-assisted insight generation. The engineer works closely with analytics, engineering, and GenAI stakeholders to translate complex data into reliable, usable, and actionable insights.

Key Responsibilities:

 

Data & Analytics Enablement

  • Build and maintain datasets, metrics, and analytical views supporting dashboards, KPIs, trend analyses, and drill-down reporting

  • Develop and refine semantic layers and metric definitions to ensure clarity, consistency, and usability across analytics products

  • Support data visualization and reporting in tools such as Tableau or QuickSight

Data Engineering & Pipelines

  • Design and implement automated data ingestion and transformation pipelines for structured and unstructured data sources

  • Support taxonomy alignment, labeling, and metadata enrichment to improve discoverability and AI readiness

  • Build scalable data storage and transformation layers, including curated datasets and dbt-style models

  • Work closely with engineering teams to ensure ingestion correctness, pipeline stability, and data readiness

GenAI & Agentic AI Enablement

  • Support integration of analytics pipelines with GenAI and Agentic AI components used for insight generation and exploration

  • Apply GenAI tools to improve analytical workflows, automate repetitive tasks, and surface insights more efficiently

  • Help prepare datasets and metadata for RAG and AI-driven analytics use cases

Data Quality, Performance & Reliability

  • Apply data quality checks, validation logic, and monitoring to ensure accuracy and reliability of analytical outputs

  • Support optimization of data pipelines for performance, latency, and scalability

  • Contribute to documentation, data definitions, and usage guidance

Collaboration & Problem Solving

  • Collaborate with analysts, GenAI specialists, quality teams, and business stakeholders to deliver aligned solutions

  • Contribute to technical design discussions and solution reviews

  • Navigate ambiguous problem spaces with a structured and solution-oriented approach

Insight Generation & Business Alignment

  • Analyze complex datasets to identify relevant takeaways for the executive team

  • Validate whether analytical results align with business logic, operational realities, and customer behavior

  • Translate business requirements into clear technical specifications so analytical workflows and data outputs support underlying strategic needs

Who are you?

  • 3+ years of experience in data engineering, analytics engineering, backend engineering, or data analytics roles.

  • Strong SQL skills and experience working with analytical datasets and metric development.

  • Hands-on experience with ETL/ELT pipelines, data ingestion frameworks, and API-based integrations.

  • Experience working with both structured and unstructured data.

  • Familiarity with data visualization tools such as Tableau, QuickSight, or similar.

  • Solid understanding of data modeling, data quality, and validation practices.

Nice to have

  • Experience supporting or integrating GenAI or Agentic AI solutions within analytics or data platforms.

  • Familiarity with cloud-based data architectures (AWS preferred).

  • Experience with customer behavior analytics, usage metrics, or quality indicators.

  • Exposure to metadata management, taxonomy design, or labeled datasets for AI use cases.

Core Skills & Attributes

  • Strong analytical thinking and attention to detail in metrics definition and data interpretation.

  • Ability to communicate technical concepts clearly to both technical and non-technical audiences.

  • Collaborative mindset with comfort working across teams and disciplines.

  • Interest in applying GenAI tools to enhance analytics and data-driven decision-making.

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