Engineering Manager

Lifesight; Karnataka; On-site; INR 3,500,000 - 6,500,000; Full time

Benefits offered by this job

Health insurance

Daily breakfast

Weekday lunches

Friday team lunch

Unlimited coffee, tea & snacks

Job summary

Lifesight is seeking an Engineering Manager to lead multiple engineering squads building our marketing measurement and decision intelligence platform. You will guide architecture, drive execution, and mentor high‑performing engineers in a hands‑on leadership role.

The role emphasizes cloud‑native SaaS delivery, data pipelines, and AI‑augmented development, with cross‑functional collaboration across Product, Data Science, and Solutions teams.

Qualifications

• 8–12 years of engineering experience.
• 2–4 years of people management in product/SaaS.
• Experience with cloud platforms (GCP/AWS) and production‑ready practices.
• Experience with BigQuery and data transformation workflows.

Responsibilities

• Lead one or more engineering squads for Lifesight’s marketing measurement platform.
• Own backend architecture decisions for services, data workflows, and dashboards.
• Champion AI adoption and integration with internal tools and workflows.
• Collaborate with product, data science, and solutions teams to convert requirements into scalable features.
• Ensure security, reliability, observability, and scalability of systems.

Skills

Team leadership

Cloud platforms (GCP/AWS)

BigQuery & data pipelines

SRE/production readiness

Architecture & design

AI adoption & tooling

SaaS product development

MarTech/AdTech exposure

Tools

BigQuery

Google Cloud Platform

Job description

Lifesight is a privacy-first Unified Marketing Measurement platform that helps marketers measure, plan, and optimize marketing spend for growth. The platform brings together causal MMM, incrementality testing, incrementality-adjusted attribution, decision intelligence, and AI-driven workflows to help marketing and finance teams understand the true incremental value of marketing investments.

Role Summary

Lifesight is looking for an Engineering Manager to lead engineering teams building our marketing measurement and decision intelligence platform. This role is suited for a hands‑on engineering leader who can manage high performing engineers, guide architecture, improve execution discipline, and build scalable SaaS systems for enterprise customers.

The role will involve leading product engineering across marketing data pipelines, measurement workflows, attribution systems, MMM output consumption, experimentation modules, customer dashboards, reporting layers, AI-assisted insights, platform integrations, and production‑grade SaaS infrastructure .

The ideal candidate should be comfortable operating at the intersection of product engineering, data systems, cloud infrastructure, reliability, security, and marketing technology .

Key Responsibilities

• The Engineering Manager will lead one or more engineering squads responsible for building scalable and reliable product capabilities for Lifesight’s marketing measurement platform. The person will work closely with Product, Data Science, Marketing Science, Solutions, Customer Success, and GTM teams to convert complex customer and measurement requirements into clear engineering roadmaps and production‑ready features.
• The role will involve owning architecture decisions for backend services, APIs, data workflows, integrations, dashboards, model-output consumption layers, and customer‑facing product modules. The person should ensure that engineering solutions are scalable, secure, maintainable, observable, and aligned with product requirements.
• Champion AI Adoption: Act as the evangelist for AI‑augmented development, driving the deep integration of tools like Claude Code, Cursor, Antigarvity 2.0, and custom internal agents.
• The role will also require close partnership with implementation and solutions teams. Since Lifesight works with fragmented customer data across multiple sources, the Engineering Manager should understand data quality issues such as schema mismatches, type errors, data transformation gaps, API failures, and integration inconsistencies. This will help the team build stronger onboarding, data validation, and platform configuration workflows.

Required Skills and Experience

• The candidate should have 8 - 12 years of engineering experience , including 2 - 4 years of experience managing engineering teams in a product or SaaS environment.
• The role requires experience with cloud platforms such as GCP or AWS
• The candidate should understand production‑readiness practices such as SLOs, incident response, root cause analysis, postmortems, backup and restore testing, capacity planning, performance tuning, and disaster recovery .
• Experience with BigQuery, Google Cloud Platform, data transformation workflows, marketing platform integrations, ad‑platform APIs, customer data platforms, tag managers, SDKs, pixels, or attribution systems would be valuable .

Domain Exposure

• Prior exposure to MarTech, AdTech, marketing analytics, attribution, incrementality testing, Marketing Mix Modelling, experimentation platforms, customer data platforms, or marketing measurement products would be a strong advantage.
• The candidate does not need to be a data scientist, but should be able to work effectively with data scientists and marketing science teams. The person should understand how statistical or model outputs need to be converted into reliable, explainable, and usable product experiences for business users.

The Engineering Manager should be able to coach engineers, review architecture, unblock execution, and create clarity in a fast‑moving SaaS environment. The person should balance speed with engineering discipline and ensure that the team builds systems that are not only functional, but also reliable, secure, observable, and scalable.

The role requires someone who can work with ambiguity, ask the right technical and product questions, and convert complex business problems into structured engineering solutions.

Success Measures

Success in this role will be measured through improved engineering delivery predictability, reduction in production issues, stronger release readiness, scalable platform architecture, improved data pipeline reliability, faster customer onboarding support, better observability, reduced operational toil, and successful delivery of product capabilities across marketing measurement, attribution, experimentation, and AI‑led decision intelligence.

Benefits :

Early impact – Help shape the tech stack and build products from the ground up.

Agile culture – Small teams, zero bureaucracy.

Great benefits – Competitive pay, health insurance, daily breakfast, weekday lunches, Friday team lunch, Fun O’Clock Fridays, and unlimited coffee, tea & snacks.

Required Skills

Understanding Data & APIs TypeScript Google Cloud Platform (GCP) BigQuery java python AWS