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About ThoughtFocus ThoughtFocus is a global IT services and solutions company with deep capabilities across Data & Analytics, Cloud, Modern Engineering, and Financial Services technology. Headquartered in the US with delivery centers in India, we partner with leading enterprises to build scalable, future-ready technology solutions. Our teams are built on a culture of ownership, expertise, and long-term client partnerships and talent is at the heart of how we grow. - https://thoughtfocus.com/ About the Role The Data Engineering & AI Practice Lead owns the end-to-end health of the Data Engineering and AI practice within our IT services business. This is a hybrid leadership role that combines technical depth with client-facing gravitas you will shape the practices offerings, win new business in presales, architect solutions during solutioning, and drive delivery excellence across active engagements. You will act as the go-to authority for all things data and AI inside the firm, bridging the gap between what clients need and what our engineering teams deliver. Key Responsibilities 1. Presales & Business Development Collaborate with Sales and Account Management teams to qualify and pursue data & AI opportunities. Lead client discovery workshops to uncover pain points, assess data maturity, and identify high-value AI use cases. Author and present compelling proposals, RFP responses, and capability decks tailored to each prospects business context. Define competitive pricing models, effort estimates, and engagement constructs (T&M, fixed-price, outcome-based). Build and maintain a reusable presales asset library: solution blueprints, case studies, demo environments, and ROI calculators. Represent the practice at industry conferences, webinars, and client briefings to establish thought leadership. 2. Solutioning & Architecture Lead architecture design for data platform, AI/ML, and analytics engagements from conceptual to detailed solution design. Define reference architectures across modern data stack components: ingestion, lakehouse/ warehouse, orchestration, serving, and AI/ML pipelines. Evaluate and recommend technology choices (e.g., Databricks, Snowflake, dbt, Apache Spark, Azure/AWS/GCP native services, LLM frameworks). Produce high-quality SOWs, architecture decision records (ADRs), and solution design documents. Drive POC/prototype builds to validate technical feasibility and de-risk client commitments. Stay current on emerging trends (GenAI, LLMOps, real-time streaming, data mesh, data contracts) and translate them into practice offerings. 3. Delivery Excellence Establish and govern delivery standards, engineering best practices, and quality gates across active data & AI projects. Define and enforce CI/CD, testing, data quality, observability, and documentation standards for the practice. Conduct milestone reviews and architecture governance checkpoints to identify risks early. Serve as executive escalation point for critical delivery issues; provide hands-on guidance to project teams. Build and own the practices capability roadmap: identify skill gaps, drive training, and recruit senior talent. Track practice-level KPIs (utilization, CSAT, delivery quality, reuse index) and report to leadership. 4. Practice Building & Thought Leadership Define and maintain the practices service catalog, go-to-market positioning, and tiered offering structure. Create and evangelize accelerators, frameworks, and IP assets that reduce time-to-value for clients. Mentor architects and senior engineers; run internal CoPs (Communities of Practice) for data engineering and AI. Partner with technology alliance teams (Databricks, Snowflake, AWS, Azure, Google) to deepen partnerships and co-sell. Qualifications Required 15+ years of hands-on experience in data engineering, analytics, or AI/ML with at least 3 years in a practice lead, principal architect, or senior manager capacity at an IT services or consulting firm. Deep expertise in modern data platforms: lakehouses (Databricks, Apache Iceberg), cloud data warehouses (Snowflake, BigQuery, Redshift), and orchestration (Airflow, dbt, Prefect). Proven track record of winning and delivering data/AI engagements worth $500K$5M+. .
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.