- Location
- Bangalore
- Type
- Full-time
- Department
- IT
- Seniority
- Entry
- Source
- RecruiterFlow
Description
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
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Run discovery with client business and IT stakeholders — understand what KPI they actually want, not just the one they asked for.
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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.
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Challenge requirements where they're ambiguous, unmeasurable, or will not survive contact with real store data.
Drive the design and execution of the solution
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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.
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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.
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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.
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Document decisions and trade-offs from those calls, close open questions, and make sure what gets built is what was agreed.
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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.
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Run debugging of data and pipeline issues in live deployments and drive them to root cause with engineering.
Plan and protect Data Engineering capacity
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Break incoming requirements into effort estimates and translate them into resource plans for the Data Engineering pod.
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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.
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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
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Partner with Customer Success as their technical counterpart: interpret client asks, assess feasibility, and give them realistic, defensible timelines they can commit to.
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Communicate slippage, dependencies and blockers early and in plain language.
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Keep both sides honest — pushing back on unworkable commitments and on internal estimates that don't hold up.
Manage stakeholders across the board
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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.
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Run status reviews, write up decisions and scope changes, and maintain documentation that outlives your involvement.
Bring product and delivery discipline
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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.
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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
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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.
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Demonstrated ownership of requirement gathering with external customers, and of translating those requirements into technical specs that engineers can build from.
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Experience driving technical design discussions with engineering teams and converting them into committed, documented decisions.
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Working SQL — you can query, join, aggregate and sanity-check data yourself rather than waiting for someone to pull it.
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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.
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Comfort with analytics and KPI logic — defining metrics, handling edge cases, reasoning about aggregation levels and data quality.
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Ability to plan and estimate work: breaking scope into tasks, sizing effort, and building a resourcing view you can defend.
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Strong written and verbal communication in English, with the judgement to pitch the same update differently for a client CIO and for an engineer.
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Comfort with ambiguity and with owning outcomes in a fast-moving, customisation-heavy environment.
Good to have
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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.
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Python (or similar) for scripting, data validation and automation.
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Experience with BI/visualisation tools (Power BI, Tableau, Looker, Metabase, Superset) and dashboard design.
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Familiarity with cloud data services (AWS / GCP / Azure) and orchestration tools (e.g. Airflow).
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Experience working with global clients across time zones.
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Working knowledge of Jira/Confluence or equivalent delivery tooling.
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Any exposure to computer vision, image recognition or ML-driven products.
