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Senior AI Data Engineer

SBS Software Ltd
Reading, UK
26 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
£47,000 - £70,000
Working hours
Regular working hours
Job source
www.indeed.com

Tech stack

Application Programming Interfaces (APIs) Google AdWords Agile Methodology Artificial Intelligence Amazon Web Services Microsoft Azure BigQuery Cloud Computing Software Quality Continuous Integration Data Architecture Data Validation
+34 more
Data Discovery Information Engineering Data Governance Extract Transform Load (ETL) Data Systems Data Warehousing Dimensional Modeling Google Analytics Design of User Interfaces Python (Programming Language) Machine Learning Query Optimization DataOps SQL Databases Systems Integration Unstructured Data Enterprise Data Management Google Cloud Cloud Platform System Retrieval-Augmented Generation Large Language Models Snowflake Llamaindex Generative AI Agentic-AI Data Layers Event Driven Architecture Data Management Tools for Reporting Semantic Modeling Natural Language Understanding Data Pipelines Amazon Redshift Databricks

Job description

  • Build AI-Enabled Interactive Advertising Dashboards: Architect and deploy dynamic, real-time analytics dashboards that ingest and model multi-channel advertising data (Google Ads, Meta, SEO, campaign conversions) to help clients easily read and interpret market trends and campaign performance.
  • Architect AI Data Platforms: Design, implement, and scale reliable data architectures that power advanced analytics, LLM applications, and client-facing digital products.
  • Build High-Performance Pipelines: Develop and optimize ETL/ELT data pipelines capable of processing structured, semi-structured, and unstructured data streams from ad networks, analytics tools, and CRM systems.
  • Integrate GenAI & RAG Solutions: Implement vector search workflows, embeddings, and Retrieval-Augmented Generation (RAG) architectures to enable natural language querying of market research, campaign copy, and ad performance metrics.
  • Semantic Modeling & Governance: Establish robust semantic layers, cataloguing frameworks, and data governance practices to ensure high data quality, consistency, and privacy across multi-client environments.
  • Engineer AI Agents & Automation: Design intelligent agents capable of automated ad data discovery, automated anomaly detection (e.g., sudden ROAS drop), smart query generation, and workflow automation.
  • Client & Cross-Functional Collaboration: Partner directly with client marketing teams, tech leads, product managers, UI/UX designers, and software engineers to translate complex business problems into intuitive technical deliverables.
  • Drive Engineering Excellence: Champion best practices in code quality, CI/CD for data pipelines, data observability, and responsible AI usage across the Webneon technical team.

Requirements

  • Enterprise Data Platforms: Proven experience building scalable data platforms, streaming architectures, and interactive analytics dashboards.
  • Ad-Tech Integration: Hands-on experience integrating digital marketing datasets (Google Ads, Meta API, GA4, BigQuery).
  • Advanced Python & SQL: Strong background in data modeling, query optimization, and complex analytical aggregations.
  • Cloud & Warehousing: Proficiency across major cloud ecosystems (AWS, GCP, Azure) and modern data warehouses (Snowflake, Databricks, Redshift).
  • Data Architecture: Strong grasp of dimensional modeling, semantic layers, data validation, and observability frameworks.
  • Production GenAI: Experience orchestrating and deploying Generative AI models into production using LangChain, LlamaIndex, or native APIs.
  • Autonomous AI Agents: Ability to build and integrate autonomous AI agents for automated data discovery and insight generation.
  • Client & Compliance Skills: Clear communication with non-technical stakeholders, grounded in enterprise security and GDPR compliance.
  • SBS Culture: Agile, innovative environment featuring end-to-end project ownership, training budgets, and team collaboration.

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