Candidates for this position are preferred to be based in Bangalore, India and will be expected to comply with their team's hybrid work schedule requirements.

About Wayfair

Wayfair is a destination for home, helping millions of customers find the right products for their homes. Our technology and data platforms power digital experiences, analytics and machine learning capabilities that help customers discover, explore and shop for what they need. Our data engineering organization plays a critical role in enabling personalized customer experiences, intelligent product discovery and data-driven business decisions at scale.

As Senior Engineering Manager, data Engineering you will lead Customer Technology's data engineering organization across Marketing, Storefront, and Search & Recommendations. You will define the strategic direction, build organizational capabilities and drive execution across a portfolio of data platforms and products that connect the end-to-end customer journey—from acquisition and marketing engagement through on-site discovery, shopping and conversion. You will lead through Engineering Managers and partner closely with senior technical leaders and Lead Individual Contributors to establish a cohesive technical vision, build scalable data foundations and deliver high-impact data products.

You will drive alignment across domains, ensuring customer signals and data assets are connected, trusted, discoverable and accessible to power analytics, personalization, measurement and machine learning. This role requires a combination of strong organizational leadership, technical judgment and business acumen. You will partner with senior leaders across Product, Data Science, Analytics and Software Engineering to translate business priorities into technology strategy, balance foundational platform investments with product delivery, and build a high-performing organization capable of executing reliably at scale.

What you’ll do

Lead and grow the organization

- Lead and develop engineering managers, technical leaders and teams across a broad data engineering portfolio.
- Build an effective organization through thoughtful team design, hiring, succession planning and developing future leaders.
- Set clear expectations for leaders and teams,



create accountability for outcomes and foster an inclusive culture of collaboration, learning and high standards.
- Coach leaders through complex technical and organizational challenges, helping them make sound decisions and grow their teams’ capabilities.

Set strategy and deliver outcomes

- Define the portfolio vision, strategy and multi-quarter roadmap across the teams.
- Connect investment decisions and delivery priorities to customer and business outcomes; make trade-offs explicit and align stakeholders around them.
- Establish operating mechanisms that support predictable delivery, transparent progress, early risk identification and continuous improvement across teams.
- Build solid partnerships with Product, ML Science, Analytics and Software Engineering leaders, translating shared priorities into coordinated plans and measurable results.
- Represent the portfolio in senior technology and business discussions, communicating strategy, performance, risks and investment needs with clarity.

Lead connected customer data products and platforms

- Align strategy, roadmaps and data definitions across the teams to advance Customer Technology initiatives and improve the end-to-end customer experience.
- Set technical direction with EM’s , senior engineers and architects across cloud storage, processing, orchestration and serving; ensure teams make sound trade-offs across reliability, performance, cost and speed.
- Guide reusable cross-domain data products that connect marketing engagement, storefront behavior, search and recommendation interactions and conversion outcomes.
- Ensure each domain has reliable, fit-for-purpose data foundations and products, with clear ownership and quality standards.
- Own portfolio-wide outcomes and standards for data freshness, quality, reliability, usability and cost across batch and real-time use cases; strengthen observability, governance, data contracts, lineage and discoverability, and simplify the ecosystem to support evolving analytics and AI/ML needs.




- Ensure teams have effective operating practices for service health, incident response, capacity, cost management and learning from production issues.

What you’ll need

- Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent practical experience.
- 12+ years of overall experience in data engineering or related technology roles, including at least 5 years leading large engineering teams and 2 years managing multiple Engineering Managers.
- Demonstrated success leading through managers and technical leaders, developing leaders, building effective teams and creating a culture of accountability and inclusion.
- Experience setting strategy and roadmaps for a broad engineering portfolio, aligning investment and execution to measurable business or customer outcomes.
- Strong technical judgment in data architecture and modeling, including dimensional modeling, change data capture, incremental processing and data versioning.
- Experience with cloud data platforms; experience with Google Cloud Platform, BigQuery and Google Cloud Storage is especially relevant.
- Enough hands-on technical depth to challenge designs and guide architecture across SQL/Python/Spark pipelines, data modeling, performance and cost—while enabling teams to own implementation.
- Experience with streaming technologies and services such as Kafka, Pub/Sub or Dataflow.
- Experience operating production data platforms with strong reliability, data quality, observability and service-management practices.
- Ability to build alignment across functions and communicate complex decisions, trade-offs and risks to senior technical and non-technical stakeholders.

Preferred qualifications

- Experience in e-commerce or another large-scale consumer product environment, particularly in digital customer experiences.
- Experience leading clickstream, behavioral analytics, event-data or customer interaction data organizations.
- Experience leading platform modernization or migration programs while protecting downstream service and consumer continuity.
- Exposure to NoSQL databases or business intelligence tools such as Looker, Tableau,

AtScale or Power BI.

- Experience enabling AI/ML applications through reliable, context-rich data products.

📌 Senior Manager Data Engineering (Bengaluru)
🏢 Wayfair
📍 Bengaluru