New opportunity

AI & BI Enablement

IN61 NXP India Private Limited · Bangalore, India

About this role

The AI/BI Enablement Specialist helps business and technical teams adopt self-service analytics, AI-assisted workflows and data visualization tools. The role combines platform expertise, training and solution design while promoting enterprise data standards and governance. The position is based in Bangalore, India.

Responsibilities

Act as a subject matter expert for Power BI, Tableau, Databricks, Dataiku, Copilot Studio and n8n, advising users on features, solution patterns and platform issues.
Develop and deliver training, office hours, documentation, demonstrations and hands-on workshops. Build an internal analytics community through forums, user groups, knowledge-sharing events and newsletters; use feedback to improve enablement programs.
Translate business analytics and AI needs into scalable solutions. Guide users building dashboards, workflows, machine learning pipelines and generative AI solutions, including prompt engineering, responsible AI use and integration of AI/ML models into analytical workflows.
Help design and optimize Power BI and Tableau dashboards using data modeling, DAX or LOD expressions, performance tuning and visualization practices. Advise on Databricks notebooks, Delta Lake, Spark optimization and AI/ML use cases.
Troubleshoot technical blockers and work with data engineering and platform teams on architecture, integrations, usage patterns and data governance. Demonstrate productivity improvements across analytics, data science and AI.

Required qualifications

3–7 years of experience in analytics, BI development, data science or data engineering.
Advanced Power BI proficiency in data modeling, DAX and Power Query, and Tableau proficiency in LOD expressions and dashboard optimization. Hands-on Databricks experience with Spark, Delta Lake, SQL and Python notebooks; practical experience with Copilot Studio and n8n for visual flows, AI/ML pipelines, automation or agents, and deployment.
Understanding of data warehousing, semantic modeling and ETL/ELT design; strong communication, teaching and documentation skills; and ability to turn business requirements into technical solutions.

Preferred qualifications include enterprise BI platform implementation or management, Azure and AWS knowledge, familiarity with MLOps, CI/CD and governance frameworks, experience leading workshops or internal communities, and certifications in Power BI, Tableau, Databricks or Dataiku. NXP offers online and offline learning opportunities.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Power BITableauDatabricksDataikuCopilot Studion8nDAXPower QueryTableau LOD expressionsApache SparkDelta LakeSQLPythonData modelingSemantic modelingData warehousingETLELTDashboard optimizationData visualizationMachine learning pipelinesGenerative AIPrompt engineeringResponsible AIAI agentsWorkflow automationData governanceTechnical trainingDocumentationSolution design

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enable self-service adoption
The role delivers training, office hours, documentation, demos and workshops, then uses user feedback to improve them. It also builds an internal analytics community through forums and knowledge-sharing events.
Turn business needs into scalable solutions
The posting calls for partnering with business teams to shape analytics and AI needs into dashboards, workflows, machine-learning pipelines and GenAI solutions that users can build and sustain.
Guide BI and data-platform design
Users need practical help optimizing Power BI and Tableau dashboards through modeling, DAX or LOD expressions and performance tuning, as well as guidance on Databricks notebooks, Delta Lake and Spark.
Support responsible AI and automation
The specialist advises on Copilot Studio, n8n and Dataiku capabilities, including solution patterns, prompt engineering, responsible AI use and integrating AI/ML models into analytical workflows.
Resolve blockers within enterprise standards
The role troubleshoots platform issues and works with data engineering and platform teams on architecture, integrations, usage patterns and data governance.

Likely challenges to think through

Balancing breadth with useful depth
Likely challenge, not a stated employer problem: supporting six named platforms could make it difficult to diagnose specialized issues while remaining responsive to users. Prepare a way to triage requests, explain when to investigate further and identify when to involve platform specialists.
Keeping self-service governed
Likely challenge: faster user-built dashboards and AI workflows may conflict with consistent data definitions, performance and responsible use. Prepare an example approach that gives users reusable patterns while checking modeling, governance and AI-use considerations.
Scaling enablement beyond individual support
Likely challenge: office hours and troubleshooting can consume time needed for workshops, documentation and community building. Prepare to discuss how recurring questions could become reusable guidance and how feedback could help prioritize improvements.

Practice your answers

Technical practice

How would you design a reusable Power BI semantic model from warehouse data?

What to coverSuggested preparation: Explain how you would define measures and dimensions, choose between ETL and ELT work, and use Power Query for appropriate transformations. Discuss how consistent definitions support self-service reporting and governance.

How would you investigate a DAX measure that returns unexpected totals?

What to coverSuggested preparation: Walk through filter context, relationships, and the measure’s intended business definition. Explain how you would validate the result at different levels of a visual before changing the calculation.

When would you use a Tableau LOD expression?

What to coverSuggested preparation: Describe a calculation that needs a level of detail different from the view and explain how filters could affect its result. Contrast that approach with changing the underlying data model.

How would you improve a slow or hard-to-use BI dashboard?

What to coverSuggested preparation: Separate performance diagnosis from visualization design: inspect the model, calculations, data volume, and visual choices. Explain how you would test whether changes improve responsiveness without obscuring the business question.

How would you diagnose a slow Spark workload in Databricks?

What to coverSuggested preparation: Explain how you would examine execution behavior, data layout, and expensive transformations before proposing an optimization. Discuss the trade-off between improving performance and adding pipeline complexity.

How would you structure a Databricks workflow using notebooks and Delta Lake?

What to coverSuggested preparation: Describe roles for SQL or Python notebooks, data preparation, and Delta Lake tables in an analytical workflow. Address how you would make outputs understandable and reliable for downstream BI or AI/ML users.

How would you decide whether a request suits Copilot Studio or n8n?

What to coverSuggested preparation: Compare an agent-oriented interaction with a workflow-orchestration or automation need, then describe the inputs, integrations, and deployment considerations you would clarify. Treat the platform choice as dependent on the use case rather than assuming either tool is always preferable.

How would you guide a user building an AI/ML pipeline with a low-code platform such as Dataiku?

What to coverSuggested preparation: Outline how you would clarify the analytical goal, data inputs, model outputs, and how those outputs enter an existing workflow. Explain what users need documented to understand and maintain the solution.

How would you evaluate a generative AI feature in an analytics workflow?

What to coverSuggested preparation: Discuss prompt clarity, testing outputs against the intended task, and responsible handling of data and potentially unreliable results. Explain when a generated answer needs human review rather than direct use in a decision.

How would you resolve an integration or governance blocker in a self-service analytics solution?

What to coverSuggested preparation: Start by isolating the user’s symptom, the data flow, and the relevant platform boundary. Explain how you would work with data engineering or platform teams to choose a scalable fix that preserves enterprise data standards.

Behavioural practice

Tell me about a time you taught a complex analytics concept to users with different technical backgrounds.

What to coverSuggested preparation: Use situation, action, and result to describe the audience’s needs, how you adapted a workshop or documentation, and what users could do afterward. Focus on evidence of understanding rather than attendance alone.

Describe a time you turned an ambiguous business request into a technical solution.

What to coverSuggested preparation: Use situation, action, and result to show the questions you asked, the trade-offs you explained, and how you confirmed the solution met the underlying need. Distinguish what the business requested from what you ultimately designed.

Tell me about a technical blocker you resolved with another team.

What to coverSuggested preparation: Use situation, action, and result to explain how you diagnosed the issue, coordinated with data engineering or platform colleagues, and communicated progress to affected users. Be clear about your own contribution and the outcome.

Describe how you used user feedback to improve an enablement program.

What to coverSuggested preparation: Use situation, action, and result to show how you identified pain points, prioritized changes to training or support, and checked whether those changes helped. A concrete feedback signal is more useful than a general claim that users were satisfied.

Tell me about a time you encouraged adoption of a new analytics or AI approach without losing sight of standards.

What to coverSuggested preparation: Use situation, action, and result to explain the proposed productivity benefit, the concerns you addressed, and how you supported responsible use or data governance. Avoid presenting adoption alone as proof of value.

Sharpen your knowledge

Topics to prepare

Data architecture and semantic modeling
The posting requires understanding of data warehousing, semantic modeling, and ETL/ELT design. Revision exercise: sketch how raw data becomes a governed dataset and semantic model for a self-service dashboard; explain where transformations belong and why.
BI modeling and dashboard performance
The role explicitly calls for Power BI data modeling, DAX and Power Query, plus Tableau LOD expressions and dashboard optimization. Revision exercise: explain how a metric’s definition and calculation level affect results, then identify likely causes of a slow dashboard.
Databricks workflow design
The posting names Spark, Delta Lake, SQL and Python notebooks, including Spark optimization. Revision exercise: diagram a notebook-based analytics workflow and explain how you would investigate an inefficient Spark step.
AI-assisted workflows and responsible use
The role covers Copilot Studio, n8n, AI/ML pipelines, agents, prompt engineering and responsible AI use. Revision exercise: outline an AI-assisted analytical workflow, including its inputs, review points and safeguards for data use.
Governed self-service enablement
The posting emphasizes enterprise data standards, troubleshooting, business-to-technical translation and user feedback. Revision exercise: take an ambiguous dashboard request and explain the questions, governance checks and support guidance you would give before proposing a solution.

Skills to revise

Build and explain a Power BI report
Practice using Power Query to prepare a small dataset, modeling it, writing a DAX measure and checking report performance. These are explicitly named Power BI capabilities for the role.
Optimize a Tableau dashboard
Practice creating an LOD expression for a defined metric, checking its result against the intended calculation level, and simplifying a slow visualization. The posting specifically calls for Tableau LOD proficiency and dashboard optimization.
Work through a Databricks notebook
Practice using SQL and Python in a notebook with Spark and Delta Lake data, then describe one performance investigation. The posting requires hands-on Databricks experience across these components.
Demonstrate an automation or agent flow
Practice walking through a small visual flow in Copilot Studio or n8n: inputs, decision steps, outputs, deployment considerations and a failure case. Both tools and practical experience with flows, automation or agents are named in the posting.
Advise on Dataiku’s place in a solution
Suggested preparation: compare when you would discuss Dataiku, Databricks or a BI tool for a sample analytics need, and identify what you would verify before advising a user. The posting names Dataiku among the platforms this role supports but does not specify particular Dataiku features to master.
Deliver a short enablement session
Practice a brief dashboard or workflow demonstration with a one-page user guide, then prepare answers to two likely troubleshooting questions. Training, documentation, workshops, office hours and resolving user blockers are explicit responsibilities.

Your preparation checklist

Prepare a requirements-to-solution walkthrough
The role translates business analytics and AI needs into scalable solutions. Prepare one example showing the initial need, design decisions, stakeholder input and outcome; use a hypothetical case if needed and label it as such.
Build a BI dashboard walkthrough
The posting emphasizes Power BI data modeling, DAX and Power Query, plus Tableau LOD expressions and dashboard optimization. Prepare diagrams or sanitized examples showing your approach to each, including a performance or visualization trade-off.
Rehearse a Databricks design exercise
Prepare a brief architecture sketch connecting ETL/ELT, a semantic model and a Databricks workflow. Be ready to explain SQL or Python notebooks, Delta Lake and Spark optimization choices, as called for in the posting.
Map an AI-assisted workflow
The role covers Copilot Studio, n8n, Dataiku and AI/ML-enabled analytics. Sketch a possible visual flow or agent, its deployment and integration points, and how you would address prompt quality and responsible AI use. Present the design as a preparation exercise, not an NXP system.
Draft a short enablement session
The posting calls for workshops, demos, documentation and office hours. Prepare a 10-minute teaching outline, a one-page user guide and a practical exercise for a self-service analytics topic.
Prepare a blocker-resolution example
The role involves troubleshooting and working with data engineering and platform teams on architecture, integrations and governance. Prepare a concise example covering diagnosis, collaboration, data-management checks and the resolution or recommendation.
Write focused questions for the employer
Ask which platforms and user groups need the most enablement, how feedback shapes training, and how the team coordinates governance and platform support. These are questions to clarify the role, not assumptions about NXP's current processes.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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