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Salary
≈ $26k – $48k per year (Estimated)
Location
Hybrid (Bengaluru, India)
Seniority
Staff · 5+ years exp

Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Sep 18, 2026. Pearson scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Pearson is a London-based learning company that publishes educational courseware, runs assessments and qualifications, and provides digital learning, English-language and workforce skills services. Founded in 1844 and listed in London and New York, it employs more than 20,000 people, and its UK Assessment and Qualifications arm delivers nearly four million exam results a year, including GCSE, A-Level and BTEC. It hires for software and data engineering, product management, channel sales, partnership management, payroll, HR operations and tutoring roles across India, the UK, the US, Poland, Spain, Israel and the Philippines.

Job Title: Staff Data Engineer

About the Role

We are seeking an Analytics Engineer to design, build, and operate our analytics and automations as well as build of AI-powered automations and copilots using governed enterprise data. This role is responsible for delivering high-quality Power BI reporting, establishing and maintaining Microsoft Fabric and/or GCP BigQuery, and building business automations and applications using Python, Power Automate and Power Apps.

You will be part of the Cloud & Service Management organization helping to evolve our self-service analytics, scalable data architecture, and automations-while ensuring security, performance, and governance across the platform.

This is a hands-on role with ownership of both solution delivery and platform best practices.

Shift & Work Location: The candidate will work US shift timings and remotely for the initial 6 months. From month 7 onwards, the role transitions to UK/US business hours with a hybrid working model from the office.

Key Responsibilities

Analytics & Reporting

  • Design, develop, and maintain reports and dashboards
  • Build and optimize semantic models using strong dimensional modeling (star schema)
  • Write and tune DAX measures with a focus on performance and usability
  • Implement Power BI and Looker deployment pipelines and promote content across environments

Microsoft Fabric Platform

  • Establish and maintain Microsoft Fabric architecture, including:
    • Lakehouse and/or Warehouse
    • Dataflows Gen2
    • OneLake data organization
  • Manage Fabric capacities, workspaces, and permissions
  • Monitor performance, cost, and reliability of Fabric workloads
  • Develop and maintain Python-based data transformations and notebooks within Fabric
  • Use Python for data preparation, enrichment, validation, and advanced analytics
  • Define and enforce data modeling and medallion architecture standards

Automation & Applications

  • Build and maintain automation flows for business processes, approvals, and integrations
  • Work with Dataverse, connectors, and security roles
  • Implement error handling, logging, and operational support patterns

Platform Governance & Operations

  • Define Dev/Test/Prod environment strategy for reporting and automation platform
  • Implement Application Life best practices (solutions, pipelines, source control where applicable)
  • Establish governance standards to prevent platform sprawl
  • Partner with security and IT teams on access control and compliance
  • Provide guidance and enablement to analysts and citizen developers

Collaboration & Leadership

  • Translate business requirements into scalable technical solutions
  • Contribute to platform roadmap and continuous improvement efforts

Agentic AI & ML Enablement

  • Design and deliver agentic AI solutions that automate multi-step business workflows (tool use, planning, and human-in-the-loop approvals) using enterprise data and governed actions.
  • Build RAG (retrieval-augmented generation) patterns over Fabric/OneLake (document ingestion, chunking, embeddings, retrieval evaluation) to power analytics copilots and self-service Q&A.
  • Develop and operate ML pipelines (feature engineering, training, evaluation, batch/real-time inference) using Python and approved ML frameworks.
  • Establish LLMOps/ModelOps practices: prompt/version control, offline evaluation, regression testing, monitoring (quality, drift, cost, latency), and safe rollback.
  • Implement AI security and governance: data access controls, prompt/data leakage prevention, PII handling, model risk reviews, and audit logging for agent actions.
  • Partner with stakeholders to identify high-value use cases and deliver measurable outcomes (time saved, defect reduction, SLA improvements).

Required Qualifications

  • 5+ years of experience in analytics, BI, or data engineering roles
  • 3+ years of hands-on Power BI development experience
  • Strong experience with Microsoft Fabric (Lakehouse, Warehouse, Dataflows)
  • Proficient in DAX, SQL, and data modeling
  • Hands-on experience with:
    • Power Automate (cloud flows, approvals, integrations)
    • Power Apps (Canvas apps)
    • Dataverse
  • Hands-on Python experience delivering ML or GenAI solutions in production (notebooks-to-service, APIs, scheduled jobs, or integrated automations).
  • Working knowledge of RAG concepts (embeddings, vector search, retrieval, grounding, evaluation).
  • Experience implementing monitoring and testing for data/ML/GenAI systems (data quality checks, model/prompt evaluation, logging/telemetry).
  • Experience managing environments, security, and deployments
  • Strong understanding of data governance and analytics best practices

Preferred Qualifications

  • Experience designing enterprise-scale analytics platforms
  • Familiarity with Azure services (Azure SQL, Data Factory, Synapse)
  • Familiarity with GCP BigQuery and Looker
  • Experience with CI/CD concepts for Power BI and Looker
  • Power Platform or Microsoft analytics certifications
  • Experience working in a Center of Excellence (CoE) model
  • Experience with Azure OpenAI / Azure AI Foundry (or equivalent) and enterprise deployment patterns.
  • Experience with orchestration frameworks (e.g., Semantic Kernel, LangChain, Autogen) and tool/function calling.
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