Data Analytics Manager

Role - Data Analytics Manager

Location - Bangalore (on site) ABOUT THE ROLE -

The Manager – Data Analytics is a critical role in growth journey. This is a high ownership leadership position responsible for building and scaling the company’s analytics foundation that powers decision-making across all cross-functional teams. This leader will work closely with founders, business leaders, product and engineering teams to ensure the company achieves its business goals in a secure, reliable, compliant, and cost-efficient manner, without sacrificing speed or innovation. The role carries direct accountability for reducing time-to-decision, strengthening trust in data, standardizing metrics, and building scalable analytics systems that evolve with the business.

This role reports to the Associate Director – Decision Science and will build and lead a growing analytics team, driving both execution excellence and long-term capability development to ensure analytics becomes a competitive advantage for the company.

Key Responsibilities: Be the bridge between data and decisions You will be the primary interface between business leaders, stakeholders, and the analytics function. Your job is to understand what the business needs, translate it into analytics work, and ensure the right problems get solved in the right order. • Collaborate with leaders across product, operations, finance, and business teams to deeply understand their data needs, pain points, and decision-making gaps. • Own the analytics intake process – gather requirements, evaluate impact, and prioritize based on available capacity and business value. • Maintain a transparent and well-communicated analytics roadmap aligned with company priorities. • Regularly engage with teams to collect feedback on how data is being used, what’s working, what’s broken, and what’s missing. • Act as a trusted advisor who helps teams ask better questions, not just answer the ones they already have. Ensure data reliability and availability, always Data that people can’t trust or access when they need it is worse than no data at all. You will own the reliability of the analytics layer. • Partner closely with Data Engineering to maintain, monitor, and improve data pipelines that feed analytics and reporting systems. • Own the SLAs for data freshness, accuracy, and availability across dashboards, reports, and self-serve tools. • Establish alerting and monitoring for pipeline failures, data quality issues, and anomalies before they impact downstream consumers. • Drive root cause analysis and remediation when data issues arise, with a bias toward systemic fixes over one-off patches. Automate to accelerate decision-making If a decision is being made manually using data that could be automated, that’s a problem you should be obsessed with solving. • Identify repetitive, manual reporting and analytics workflows across the organization and drive automation to reduce turnaround time. • Build and champion self-serve analytics capabilities – enabling teams to answer their own questions without waiting for the analytics team. • Design automated alerts, triggers, and exception-based reporting so teams are proactively informed instead of reactively requesting data. • Measure and track the time-to-insight for key business questions and continuously work to reduce it. Own data access control and governance With great data access comes great responsibility. You will ensure the right people have access to the right data, and no one has access they shouldn’t. • Define and enforce data access control policies across all analytics platforms, dashboards, and data stores. • Implement role-based access controls (RBAC) and ensure they are auditable and consistently applied. • Conduct periodic access reviews and ensure compliance with internal security and privacy policies. • Work with security and compliance teams to ensure data handling meets regulatory requirements including DPDP and other applicable frameworks. Invest in the right platforms and future-ready technology. The tools you choose today define the speed and quality of decisions tomorrow. Evaluate what exists, challenge what’s legacy, and invest in what scales. • Evaluate the current analytics platform stack and identify gaps, inefficiencies, and opportunities for consolidation or upgrade. • Research, evaluate, and recommend better platforms for BI, visualization, data cataloging, and analytics workflows. • Own the analytics platform roadmap – plan and execute migrations, integrations, and new platform rollouts with minimal disruption. • Stay current on emerging analytics technologies and bring informed recommendations to the table, not just what’s trending. Help the organization visualize better and faster A great dashboard is not one that has every metric – it’s one that drives the right action at the right time. • Own the standard for how data is visualized across the organization – consistency, clarity, and actionability. • Build and maintain a library of well-designed, performant dashboards and reports that serve both leadership and operational teams. • Ensure dashboards load fast, reflect fresh data, and answer the “so what?” – not just the “what.” • Train and enable teams on data literacy and visualization best practices so they become better consumers and creators of insights. Drive cost efficiency in data consumption Data at scale is expensive. You will ensure we get maximum value per dollar spent on data infrastructure and analytics platforms. • Own the cost visibility and accountability for analytics platform and data consumption spend. • Identify and eliminate wasteful queries, redundant pipelines, underutilized dashboards, and over-provisioned resources. • Establish cost governance practices – budgets, alerts, and optimization reviews at a regular cadence. • Work with Data Engineering and Platform teams to optimize storage, compute, and query costs without compromising performance or availability. Build a feedback-driven analytics culture The best analytics teams don’t just deliver reports – they continuously learn from the people they serve. • Establish regular feedback loops with all major teams to understand what’s working, what’s not, and what new data needs are emerging. • Track analytics adoption metrics – who’s using what, how often, and whether it’s driving action. • Use feedback to continuously refine the analytics roadmap, improve data products, and sunset what’s no longer useful. • Champion a culture where data is not a reporting function but a strategic capability embedded in how every team operates.

Qualifications, Skills, and Experience- We are looking for a hands-on analytics leader with a proven track record of a high ownership mindset, bias for action, and curiosity. If you are someone who actively experiments with data tools, learns from the business, and evolves with technology rather than relying on past playbooks, you will find us home. Proven credentials • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a closely related analytical field. • 4-6 years of overall experience in analytics, data engineering, or BI roles, with significant hands-on experience in building analytics platforms, dashboards, and data products. At least 1-2 years in a leadership or management role. • Startup or high-growth company experience is strongly preferred; experience in ecommerce, marketplaces, logistics, or large-scale data environments is a plus. • Strong proficiency in SQL, Python, and at least one modern BI/visualization tool (e.g., Tableau, Looker, Metabase, Power BI, Superset). • Hands-on experience with cloud data platforms (e.g., AWS Redshift, BigQuery, Databricks) and data pipeline tools (e.g., Airflow, DBT). Ability to build the team and lead from the front • Demonstrated capability to build, mentor, and scale high-performing analytics teams,especially in startup culture where leaders stay close to execution. • Demonstrated ability to roll up sleeves and execute – this role requires building dashboards, writing queries, debugging data issues, and reviewing work, not just delegating. • Ability to operate in a fast-paced, ambiguous, and evolving environment, with comfort making trade-offs between speed, depth, cost, and coverage. • Strong experience translating vague business questions into structured analytical frameworks and actionable insights. • Proven ability to take business goals (e.g., cost reduction, growth targets, operational efficiency) and convert them into analytics initiatives with measurable outcomes. Proven ability to work cross-functionally and influence without hierarchy • Experience working with product, engineering, operations, finance, and business teams – influencing without hierarchy and acting as a trusted data advisor. • Ability to communicate complex data findings in simple, compelling ways to both technical and non-technical audiences. • Experience with data governance, access control policies, and compliance frameworks is a strong plus.