Lead Data Engineer for one of our clients. Location: Bangalore. Work mode: Hybrid (3 days a week in the office). Working time zone: IST.

Client is a agile, fast-paced global platform dedicated to reimagining the movement of people and goods in real time. Driven by a relentless mission to facilitate transport, delivery, and flexible earning opportunities, the company has evolved from a ride-hailing service into a comprehensive ecosystem that handles everything from everyday mobility to multi-ton freight, meals, and essential medical supplies. With a strong commitment to continuous innovation, safety, and transparency.

Project: Owns the data engineering team's roadmap, platform architecture, and stakeholder/vendor relationships.

Responsibilities and Tasks

- Team management; Delivery Leadership: Experienced in managing a medium-to-large team of data engineers; own JIRA sprint planning and agile project management.
- Stakeholder Management: Own client relationship / stakeholder management in an onsite-offshore delivery model.
- Platform Architecture: Own data modeling/storage strategy, pipeline orchestration, and governance/security standards across the platform, setting them as team-wide best practices.
- Maintenance Reliability: Own capacity planning, backup/upgrade strategy, and incident escalation processes across the team's pipelines and infrastructure.
- Team Enablement: Guide the team on tool and technology selection (e.g., data integration tools, cloud platforms) and mentor engineers on best practices.

Ideal Candidate

- Education: Bachelor's degree required; advanced degree preferred.
- Experience: 7+ years in data engineering, including 2+ years managing a team of roughly 4-10+ engineers and owning platform architecture decisions.
- Technical Skills: Full command of the senior-level stack, plus experience evaluating and selecting data platform vendors and tools (e.g., Databricks,



Snowflake, cloud-native ML platforms) at an organizational level.
- Domain: Deep expertise in MLOps/data-platform architecture, including feature pipelines, model training data platforms, and inference-serving layers.
- Leadership: Proven experience running JIRA/agile sprint planning for an engineering team; owns client relationship and stakeholder management in an onsite-offshore delivery model; experience assessing and interviewing engineering candidates.
- AI Literacy Enablement: AI engineering strategy: building a data platform that reliably and scalably supports AI product development.
- AI Literacy Enablement: AI-first platform design: architects and governs AI-ready data infrastructure - real-time and batch feature pipelines, model training data platforms, vector databases, and inference serving layers.
- AI Literacy Enablement: AI tooling governance: evaluates, approves, and governs the use of AI engineering tools across the team; balances productivity gains against data security, compliance, and reliability risks.
- AI Literacy Enablement: AI literacy programme: drives a structured AI upskilling programme for the engineering team; identifies capability gaps, commissions training, and mentors senior engineers on AI-native engineering practices.
- AI Literacy Enablement: Vendor ecosystem partnerships: leads evaluation of AI-native data platform vendors (e.g. Databricks, Snowflake AI, Google Vertex AI, AWS SageMaker); negotiates commercial and technical terms aligned with client's needs.

Nice to have

- Cloud architect-level certification; agile/Scrum leadership certification (e.g., CSM, PMI-ACP).

English: Upper-Intermediate or higher

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📌 Lead Data Engineer (Bengaluru)
🏢 Instinctools
📍 Bengaluru