YES MADAM Job Description - Data Engineering & Analytics Lead

Location : Sector 63, Noida

Experience : 8 - 12+ Years

Employment Type : Full-Time

Work Mode : Work From Office (WFO)

Function : Data Engineering, Analytics & Business Intelligence

About YES MADAM

YES MADAM is a leading D2C at-home beauty and wellness services platform, serving lakhs of customers across the country. We are a fast-moving, insight-driven organization focused on building scalable technology and delivering exceptional customer experiences.

About the Role

We are looking for a Data Engineering & Analytics Lead to build and lead our data engineering and analytics capabilities. This role will be responsible for designing scalable data pipelines, developing a reliable data platform, establishing data quality standards, and enabling actionable insights across Product, Technology, Marketing, Operations, Finance, and Business teams. The ideal candidate will combine strong hands-on data engineering expertise with analytical thinking and leadership capabilities. They should be able to translate business and product requirements into robust data solutions that support product optimization, customer insights, operational efficiency, and business growth.

Key Responsibilities

1. Data Engineering & Architecture :

- Design and develop scalable, reliable, and cost-efficient data architectures and data pipelines.

- Build and maintain ETL/ELT workflows to ingest, transform, validate, and process data from multiple sources.

- Develop and optimize data warehouses, data lakes, and analytical data models.

- Establish data architecture standards to support growing data volumes and real-time business requirements.

- Ensure data availability, consistency, accuracy, and reliability across systems.

2. Product & Business Analytics :

- Partner with Product, Engineering, Marketing, and Business teams to identify analytical requirements and build data-driven solutions.

- Develop frameworks to track product metrics, customer behaviour, user journeys, conversion funnels, retention, repeat purchases, and cohort performance.

- Enable A/B testing, experimentation, feature performance analysis, and product decision-making.

- Build analytical models to identify business opportunities, customer trends, operational bottlenecks, and growth drivers.

- Translate complex datasets into actionable insights and recommendations for leadership.

3. Business Intelligence & Reporting :

- Build and maintain dashboards, reports, and self-service analytics capabilities.

- Define and standardize KPIs across product, customer experience, marketing, and operations.

- Enable leadership teams to monitor business performance through reliable and timely reporting.

- Improve reporting accuracy, reduce manual reporting efforts, and establish a single source of truth for key business metrics.

4. Data Quality, Governance & Security :

- Establish data quality checks, monitoring mechanisms, and pipeline observability.

- Define data governance practices, documentation standards, data lineage, and access controls.

- Ensure appropriate handling of customer and business data in accordance with applicable privacy and security requirements.

- Identify and resolve data discrepancies, pipeline failures, and performance bottlenecks.

5. Team Leadership & Stakeholder Management :

- Lead, mentor, and develop data engineers and analytics professionals.

- Work closely with Product Managers, Engineering Leaders, Business Heads, and senior leadership.

- Prioritize data initiatives based on business impact, technical feasibility, and organizational needs.

- Drive a culture of data ownership, analytical rigor, and continuous improvement.

Must-Have Skills & Experience :

- 8 - 12+ years of experience in data engineering, analytics engineering, business intelligence, or related data-focused roles.

- Strong hands-on expertise in SQL, Python, data modelling, and ETL/ELT pipeline development.

- Experience designing scalable data pipelines and modern data warehouse architectures.

- Strong understanding of data warehouses, data lakes, dimensional modelling, and analytical data models.

- Experience with cloud data platforms such as AWS, GCP, or Azure.

- Hands-on experience with technologies such as Apache Spark, Airflow, dbt, Kafka, or equivalent tools relevant to the organization's architecture.

- Experience with BI and visualization tools such as Power BI, Tableau, Looker, or equivalent.

- Strong understanding of product analytics, funnel analysis, cohort analysis, retention, experimentation, and business KPI frameworks.

- Experience leading projects and collaborating with cross-functional stakeholders.

- Strong problem-solving, communication, and people-management skills.

Good-to-Have Skills :

- Experience in a product-based organization, consumer internet, D2C, e-commerce, marketplace, or high-growth startup.

- Exposure to product analytics tools such as Mixpanel, Amplitude, or Firebase Analytics.

- Experience with real-time data processing, streaming architectures, and event-driven systems.

- Knowledge of data orchestration, CI/CD for data pipelines, data observability, and automated data testing.

- Experience with customer segmentation, personalization, recommendation systems, or predictive analytics.

- Exposure to AI/ML data pipelines, feature engineering, and data infrastructure for AI applications.

- Experience building a data platform or analytics function from the ground up.

Key Success Metrics :

- Reliability, performance, and scalability of data pipelines.

- Data accuracy, quality, freshness, and availability.

- Reduction in manual reporting and time required to generate insights.

- Adoption and effectiveness of dashboards and analytics solutions.

- Improved visibility into product, customer, and business KPIs.

- Ability to translate business problems into actionable data insights.

- Team productivity, stakeholder satisfaction, and timely delivery of data initiatives.

Ideal Candidate Profile :

- The ideal candidate is a hands-on data leader who can build robust data infrastructure while enabling meaningful product and business insights. They should be comfortable working in a fast-paced product environment, balancing technical execution with strategic thinking, and building data capabilities that directly support business growth and product decisions.