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 practice’s 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 prospect’s 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 contemporary 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 practice’s 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 practice’s 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+.

Strong cloud fluency across at least two hyperscalers (AWS, Azure, or GCP) including their native data and AI services.

Experience architecting and delivering ML/AI solutions including feature engineering, model training, deployment, and monitoring pipelines.

Excellent written and verbal communication; able to present technical concepts to C-suite and business stakeholders.

Experience with GenAI/LLM-based solutions: RAG architectures, fine-tuning, prompt engineering, and LLMOps.

Communication and presentation skills must be excellent.

Excellent stakeholder management skills.

Job location will be Bangalore (preferable)or Hyderabad

Preferred

Cloud certifications: AWS Solutions Architect Professional, Azure Data Engineer Associate, GCP Professional Data Engineer, or equivalent.

Experience building and scaling a practice or CoE from the ground up.

Hands-on exposure to data governance platforms(Collibra, Alation, Unity Catalog) and data observability tools (Monte Carlo, Great Expectations).

Familiarity with data mesh and data productthinking; ability to coach clients through organizational change.

Engineering Degree, MBA or equivalent post-graduate degree is a plus.

📌 Data Engineering & AI Practice Lead (Bengaluru)
🏢 ThoughtFocus
📍 Bengaluru