Head of Intelligent Commerce
ADA
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Job summary
ADA is seeking the Head of Data Platform to own the end-to-end Data Platform, including architecture, data model, and client-facing data delivery. This senior leadership role combines hands-on engineering with product thinking to drive scale, reliability, and performance across the platform powering our global data and AI experiences.
You will lead engineering and product teams across Vietnam and India, setting technical direction, building a cohesive data model, and ensuring data quality and
Qualifications
- 10–15+ years of experience in software/data engineering or related fields.
- Significant experience owning and scaling data platforms, data products, backend systems, or technology platforms.
- Strong hands-on backend engineering and system architecture experience, especially distributed systems.
Responsibilities
- Own the Data Platform architecture and technical roadmap.
- Own the unified data model for consistent cross-source analysis.
- Optimise data collection and processing at scale with Python microservices and Airflow.
- Lead database architecture and data routing to client-specific warehouses.
- Lead and develop engineering teams across Vietnam and India.
- Translate user needs into clear product requirements and roadmaps.
- Own data accuracy as a product outcome and ensure timely, reconciled data.
Skills
Tools
Job description
10 - 15 Years
Full Time
Job Description
About ADA
ADA is the Data and AI Experience Company. Present in 14 markets globally, we build intelligent experiences that power enterprise growth, spanning identity and authentication, personalization, commerce, and data & AI foundations; where every touchpoint earns trust, every interaction creates value, and every decision runs on real-time intelligence, so enterprises can stay ahead and deliver measurable outcomes. Our mission is to build the world's most intelligent growth platform for every client we serve. We work with over 1,500 global brands across CPG, Retail, Telco, BFSI, and Healthcare.
Role Overview
The Head of Data Platform owns ADA’s Data Platform end to end — both its technical direction and product direction .
This is a senior, hands‑on leadership role accountable for the architecture, scalability, reliability, and performance of the platform that powers the business. At the same time, the role owns what is built on top of that platform: the product roadmap, requirements, user experience, data quality, and client adoption.
The platform is built on a proprietary technology stack comprising Python-based microservices for data collection at scale, an Airflow-based processing framework, a unified data model that normalises data across sources, distributed client-specific data warehouses, and a lightweight front‑end for data exploration and visualisation.
The Head of Data Platform brings together responsibilities that currently sit across multiple teams — including requirements, data loading, front‑end testing, engineering, and delivery. The role establishes a single accountable chain from client need → product requirement → technical solution → release → adoption and verified outcome .
You will lead a combined engineering and product team across Vietnam and India , working closely with the CTO, Product, Business, Analytics, Project Management, and client‑facing teams.
This role requires a combination of deep backend engineering expertise, strong system‑design judgement, product thinking, commercial understanding, and exceptional cross‑functional leadership .
1. Own the Data Platform architecture and technical roadmap
Define and own the long‑term technical direction of the platform, making scalable architectural decisions across data collection, processing, storage, routing, and serving as business priorities evolve.
2. Own the unified data model
Oversee schema and table design for the central data model that enables consistent analysis across multiple platforms and sources.
Ensure the model remains flexible and future‑proof as new data types, platforms, markets, and sources are introduced, while maintaining consistency and usability across the ecosystem.
3. Optimise data collection and processing at scale
Drive the performance, reliability, and scalability of the data collection pipeline, including Python-based microservices, Airflow-based orchestration, and multithreaded extraction.
Continuously optimise queue management, processing patterns, and system performance to minimise overhead and ensure fast, reliable data delivery.
4. Own database architecture and data routing
Lead database and relational schema planning for high‑volume data ingestion, processing, and querying.
Own the routing of processed data into client‑specific data warehouses, ensuring customers retain appropriate control and ownership of their data while meeting privacy, security, and data sovereignty requirements.
5. Lead and develop the engineering organisation
Lead, coach, and mentor engineering teams across Vietnam and India.
Establish strong technical standards, architecture practices, code quality, engineering discipline, and review processes while building a culture of ownership, accountability, and continuous improvement.
6. Build deep understanding of users and customer needs
Develop a strong understanding of both internal and external users.
Work closely with Account Management, Commercial, Analytics, Project Management, and other internal teams to understand how the platform is used, while engaging directly with clients to understand how brands work with and make decisions from their data.
Translate these insights into clear product requirements, scope, user stories, and acceptance criteria.
7. Own the Data Platform product roadmap and prioritisation
Own product prioritisation and roadmap decisions based on customer needs, commercial impact, technical considerations, and platform scalability.
Make explicit and defensible trade‑offs between new capabilities, new data sources, reliability, scalability, technical debt, and engineering investment.
8. Own data accuracy as a product outcome
Take end‑to‑end accountability for ensuring that data delivered to clients is accurate, complete, timely, and reconciled to source.
Partner with QA and QC to establish appropriate release gates and own the resolution path from a reported data discrepancy through investigation, prioritisation, remediation, and production release.
9. Own the client‑facing product experience
Own the front‑end and visualisation experience through which clients access, explore, understand, and act on their data.
Ensure the product experience evolves alongside the underlying data model and platform capabilities, and remain accountable for adoption and client value after release — not simply delivery.
10. Act as the bridge between technology and business
Translate complex technical and architectural concepts into clear business language and influence decisions across Business, Product, Technology, Analytics, and Project Management.
Work closely with stakeholders to ensure every request is properly qualified, prioritised, routed, built, tested, and ultimately validated against a measurable business or client outcome.
Objective: Ensure the platform operates reliably and delivers data within agreed service levels.
Measures:
- Data delivered within agreed SLAs
- Reduction in platform incidents and data delivery failures
Objective: Deliver the agreed product and technical roadmap while increasing the platform’s ability to scale.
Measures:
- Achievement of quarterly roadmap milestones
- Ability to onboard new data sources and clients without significant re-architecture
- Improved platform performance and scalability
Objective: Ensure platform capabilities are actively used and generate measurable client value.
Measures:
- Active usage of the platform and front‑end
- Adoption of newly released capabilities
- Client engagement and usage against agreed targets
- Clear ownership and action plans for declining adoption metrics
Objective: Reduce the time required to convert qualified customer or business requirements into validated production capabilities.
Measures:
- Time from qualified requirement intake to production release
- Reduction in delivery cycle time as the function matures
- Percentage of releases successfully validated against agreed acceptance criteria
Objective: Build a high‑performing engineering and product organisation while maintaining a healthy and maintainable technology estate.
Measures:
- Code quality and engineering standards
- System maintainability and technical debt
- Reduction in incident rates
- Engineering productivity and delivery discipline
- Team development, engagement, and retention
- 10–15+ years of experience in software engineering, data engineering, platform development, product development, or a related field.
- Significant experience owning and scaling data platforms, data products, backend systems, or technology platforms in complex enterprise environments.
- Strong hands‑on experience with backend engineering and system architecture , particularly distributed systems, data processing, APIs, microservices, databases, and high‑volume data pipelines.
- Strong understanding of data modelling, schema design, data processing, data quality, and data architecture .
- Experience designing and operating scalable data collection or ingestion systems.
- Strong product mindset, with the ability to translate customer and business needs into clear product requirements and prioritised roadmaps.
- Demonstrated ability to own a significant product, platform, or technology area with a high degree of autonomy.
- Experience leading and developing distributed engineering teams , preferably across multiple countries or locations.
- Proven ability to work effectively across Engineering, Product, Business, Analytics, Commercial, and Project Management functions.
- Strong commercial and stakeholder‑management skills, with the ability to make pragmatic trade‑offs between business value, technical complexity, scalability, reliability, and delivery speed.
- Excellent written and verbal communication skills, including the ability to explain complex technical concepts to non‑technical stakeholders.
- Experience building enterprise data platforms or B2B SaaS products .
- Experience in e‑commerce, Retail, CPG, Telco, or BFSI .
- Experience with large‑scale data collection, web data extraction, or multi‑source data integration.
- Experience with Python, Airflow, microservices, relational databases, distributed systems, and cloud‑based data platforms .
- Experience owning both technology and product outcomes .
- Exposure to AI/ML, analytics, or data products.
- Experience working with client‑specific data environments and data privacy or sovereignty requirements.
We are looking for a leader who:
- Thinks platform and product together — understands that technical excellence only matters when it enables customer and business outcomes.
- Takes end‑to‑end ownership — does not stop at requirements, architecture, or delivery, but remains accountable for the finished outcome.
- Has strong technical judgement — can go deep when required while making pragmatic architectural and engineering decisions.
- Thinks commercially — understands how platform investments translate into client value, revenue, scalability, and operational efficiency.
- Operates with urgency and accountability — makes decisions, removes blockers, and drives teams toward measurable outcomes.
- Balances strategic thinking with hands‑on execution — able to shape the long‑term platform while engaging deeply with current technical and product challenges.
- Builds strong teams — develops engineering and product talent and creates a culture of ownership, quality, experimentation, and continuous improvement.
- Communicates with influence — can move seamlessly between technical discussions with engineers and strategic discussions with senior business stakeholders.
Reports to: Chief Technology Officer (CTO)
Team: Engineering and Product teams across Vietnam and India
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Job Snapshot
Updated Date
28-09-2026
Job ID
JOB_318
Department
Location
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