About ParallelDots

ParallelDots builds computer-vision technology that tells global CPG brands exactly what is happening on retail shelves. Our flagship product, ShelfWatch, turns a single shelf photo into real-time execution KPIs — on-shelf availability, share of shelf, planogram and promo compliance, price-tag presence, POSM condition — so field teams can fix problems before they leave the store. Alongside it, Saarthi trains recognition models for new SKUs in under 48 hours, and ShelfWatch Camera brings autonomous, always-on shelf monitoring to the aisle.

The platform is deployed in 50+ countries, covers 2.5M+ retail outlets, and processes 15M+ images every month for brands including Unilever, Mondelez, Heineken, BAT, Kraft Heinz, Danone, Asahi, SC Johnson and ITC.

About the role

You'll manage the team who owns the end-to-end analytics pipeline for a portfolio of global CPG clients — configuring standard KPIs, defining and building custom ones, and implementing the right integration approach to exchange data with client systems in near real time. It's a role that sits directly between the customer and the engineering org, and it's equally business logic and systems design.

What you'll do

Own client requirements, end to end

- Run discovery with client business and IT stakeholders — understand what KPI they actually want, not just the one they asked for.

- Translate commercial intent ("we need to know if our must-carry SKUs are on shelf in modern trade") into precise, testable technical specifications: KPI definitions, formulas, edge cases, data granularity, refresh cadence, and acceptance criteria.

- Challenge requirements where they're ambiguous, unmeasurable, or will not survive contact with real store data.

Drive the design and execution of the solution
- Lead the design discussions with Data Engineering and Product — you set the agenda, bring the requirement and the constraints into the room, frame the options, and drive the group to a decision rather than waiting for one to emerge.

- Own the resulting solution design for each client's analytics pipeline: which standard KPIs apply, what needs to be custom-built, how data is modelled, and how insights surface in dashboards or downstream systems.

- Steer the choice of integration approach for data exchange with client platforms — SFA, CRM, ERP, BI tools,



merchandising and field-audit systems — balancing latency, volume, reliability and the client's technical maturity.

- Document decisions and trade-offs from those calls, close open questions, and make sure what gets built is what was agreed.

- Stay hands-on through execution: SQL, APIs and platform configuration, validation and UAT. You're accountable for whether the numbers are right, not just for whether the design was sound.

- Run debugging of data and pipeline issues in live deployments and drive them to root cause with engineering.

Plan and protect Data Engineering capacity
- Break incoming requirements into effort estimates and translate them into resource plans for the Data Engineering pod.

- Maintain a forward view of demand across live clients, sequence work against business priority and contractual commitments, and flag over-commitment before it becomes a missed date.

- Track utilisation and delivery throughput; surface the trade-offs (scope, timeline, resourcing) rather than absorbing them silently.

Be the bridge between Customer Success and delivery
- Partner with Customer Success as their technical counterpart: interpret client asks, assess feasibility, and give them realistic, defensible timelines they can commit to.

- Communicate slippage, dependencies and blockers early and in plain language.

- Keep both sides honest — pushing back on unworkable commitments and on internal estimates that don't hold up.

Manage stakeholders across the board
- Hold your own in a room with a client's IT lead, their category manager, our CS team, and our engineers — and leave everyone with the same understanding of what's being built and when.

- Run status reviews, write up decisions and scope changes, and maintain documentation that outlives your involvement.

Bring product and delivery discipline




- Apply sound tech and product development lifecycle practices — requirement to spec to build to UAT to release to support — with clean handovers at each stage.

- Spot patterns across clients: when the third customer asks for the same "custom" KPI, make the case for productising it and feed that back into the roadmap.

What we're looking for

Must have
- 2+ years in a client-facing techno-functional role — technical product manager, product owner, technical consultant, solutions engineer, implementation consultant, or business analyst on data products.

- Demonstrated ownership of requirement gathering with external customers, and of translating those requirements into technical specs that engineers can build from.

- Experience driving technical design discussions with engineering teams and converting them into committed, documented decisions.

- Working SQL — you can query, join, aggregate and sanity-check data yourself rather than waiting for someone to pull it.

- Practical understanding of data pipelines and integrations: REST APIs, JSON, authentication, webhooks, batch vs. streaming, scheduled jobs, SFTP/file-based exchange, and what typically breaks in each.

- Comfort with analytics and KPI logic — defining metrics, handling edge cases, reasoning about aggregation levels and data quality.

- Ability to plan and estimate work: breaking scope into tasks, sizing effort, and building a resourcing view you can defend.

- Strong written and verbal communication in English, with the judgement to pitch the same update differently for a client CIO and for an engineer.

- Comfort with ambiguity and with owning outcomes in a quick-moving, customisation-heavy environment.

Good to have
- Exposure to retail, CPG, FMCG, retail execution, trade marketing, SFA or merchandising domains — familiarity with concepts like OSA, share of shelf, planogram compliance or perfect-store programmes is a real advantage.

- Python (or similar) for scripting, data validation and automation.

- Experience with BI/visualisation tools (Power BI, Tableau, Looker, Metabase, Superset) and dashboard design.

- Familiarity with cloud data services (AWS / GCP / Azure) and orchestration tools (e.g. Airflow).

- Experience working with global clients across time zones.

- Working knowledge of Jira/Confluence or equivalent delivery tooling.

- Any exposure to computer vision, image recognition or ML-driven products.

📌 Associate Product Manager (Bengaluru)
🏢 ParallelDots
📍 Bengaluru