🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
We are seeking a highly experienced and technically strong leader to head our Data & AI Engineering function. This role will be responsible for driving the technical vision, delivery excellence, and AI-first transformation across our data engineering and analytics teams. The ideal candidate will bring a hands-on leadership approach, combining deep expertise in data platforms and applied AI with the ability to guide teams, architect scalable solutions, and deliver measurable business outcomes. Role Summary: This position requires a strategic yet hands-on leader who can operate across data engineering, analytics, and AI, and drive the organization toward becoming a truly AI-native delivery function. The role demands a balance of technical depth, leadership capability, and business acumen to deliver impactful, scalable, and future-ready solutions. Key Responsibilities: - Define and implement the AI-first engineering strategy, including standards, frameworks, and best practices across all engagements - Lead, mentor, and scale a cross-functional team of data engineers, analysts, and integration specialists - Drive adoption of AI-assisted development tools (e.g., Cursor, Claude Code, GitHub Copilot) as an integral part of the engineering workflow - Design and deliver advanced AI solutions, including: - LLM integrations ( Large Language Model) - Prompt engineering frameworks - Agentic workflows - RAG-based architectures - Architect and oversee end-to-end data solutions, including cloud data platforms, ERP integrations, and reporting systems - Act as the technical authority, providing hands-on guidance through solution design, code reviews, and engineering best practices - Collaborate with internal stakeholders and clients to identify opportunities for AI-driven optimization and automation - Establish and enforce data governance, quality standards, and responsible AI practices. Required Experience - 10+ years of experience in data engineering, analytics, or data platform development - Minimum 5 years of hands-on experience in applied AI/ML, including LLMs and modern AI frameworks - Proven, day-to-day experience with AI-assisted development tools (such as Cursor, Claude Code, GitHub Copilot) - Demonstrated success in leading technical teams within consulting or managed services environments - Strong ability to translate technical solutions into business value for stakeholders. Mandatory Key Skills Core Technical Skills - Strong proficiency in Python and SQL (including stored procedures) - Hands-on experience with cloud data platforms (Azure, Snowflake, Databricks) - Expertise in building and managing ETL/ELT pipelines - Experience with business intelligence tools (Power BI, Tableau, Looker) AI & Advanced Capabilities - Deep understanding of: - Large Language Models (LLMs) - Prompt engineering techniques - RAG (Retrieval-Augmented Generation) architectures - Agentic AI frameworks - Experience designing and deploying production-grade AI solutions - Regular usage of AI-assisted development tools in coding and delivery workflows Leadership & Stakeholder Skills - Proven experience in team leadership, mentoring, and capability building - Strong architecture and code review expertise - Ability to communicate effectively with both technical and non-technical stakeholders Business & Consulting Orientation - Strong problem-solving skills with the ability to convert business requirements into scalable technical solutions - Experience in client-facing roles and consulting environments - Ability to articulate business impact and ROI of data and AI initiatives Preferred Qualifications (Good to Have) - Experience with ERP platforms (SAP, Dynamics, Plex) - Familiarity with workflow automation tools (e.g., n8n) - Background in leading analytics consulting firms (e.g., Tiger Analytics, Fractal, Tredence) - Contributions to open-source AI or data engineering projects. below are the Mandatory skill sets - Strong programming expertise in Python and SQL (including stored procedures) - Hands-on experience in building and managing ETL/ELT data pipelines - Experience with cloud data platforms (Azure, Snowflake, Databricks) - Solid understanding of data architecture and end-to-end data engineering workflows - Proven hands-on experience with Applied AI, including: - Large Language Models (LLMs) - Prompt Engineering - RAG (Retrieval-Augmented Generation) architectures - Agentic AI / workflow frameworks - Demonstrated experience in building and deploying AI solutions in production environments - Regular, hands-on usage of AI-assisted development tools such as: - Cursor - Claude Code - GitHub Copilot - Experience in technical leadership, including: - Leading/mentoring engineering teams - Solution architecture and .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.