Location
Bengaluru, Karnataka, India
Required Skills
About the Role
About The Role We are seeking a strategic and hands-on Director of Data Engineering to lead the development and scaling of our data platform powering next-generation PropTech products. This leader will be responsible for enabling a unified, high-quality view of property, tenant, and market data to support analytics, digital experiences, and AI-driven insights.
You will own the full data lifecycle - from ingestion of diverse real estate data sources to transformation into a Common Data Model and delivery of trusted data for product, analytics, and machine learning use cases.
Key Responsibilities
+ Ingestion of structured and unstructured real estate data (e.g., listings, transactions, IoT/sensor data, geospatial data, third-party datasets)
+ Transformation into a scalable Common Data Model (CDM) for property, tenant, and asset data
+ Data architecture and platform design (batch and real-time)
+ Fulfillment of data for internal products, analytics, and AI use cases
+ Standardization across fragmented data sources
+ Cross-product interoperability
+ Longitudinal views of assets and tenants
+ Property listings and transaction data
+ Market and pricing data
+ Building operations and IoT data
+ Property search and recommendation engines
+ Market analytics and valuation models
+ Tenant and landlord insights
+ Predictive maintenance and smart building use cases
Qualifications Required
+ Data modeling (especially domain-driven or Common Data Models)
+ ETL/ELT pipelines and orchestration
+ Distributed data processing systems
Preferred
+ Geospatial data tools (e.g., GIS systems)
+ IoT or smart building data pipelines
+ Real estate datasets (MLS, property records, market feeds)
Key Competencies
Success Metrics
Benefits
About Us
From the day we opened our doors, MRI Software has built flexible, game-changing real estate software that powers thriving communities and helps make the world a better place to live, work and play. Fulfilling that mission is only possible because of one thing: exceptional people. People like you!
Our people-first approach to PropTech is defining a new industry standard for client experiences that, quite frankly, can’t be duplicated. Experiences that deliver real value every day. And we know those experiences begin with our people.
We believe MRI is more than just a workplace; it’s a connected community of people who truly feel they belong. Whether we’re investing in employee resource groups or providing tailored resources for each person to reach their full potential, we’re passionate about creating a work environment that makes you excited to show up every single day.
At MRI, one of our core values is to
strive to amaze.
From the intelligent solutions we create to the culture we cultivate, that’s our goal every day. Because that’s what industry leaders do. Whether you’re joining as a new Pride member or rejoining us after a short time away, your talent is vital to us, our partners and our clients.
Amazing growth requires amazing employees. Are you up to the challenge?
We know confidence gap and imposter syndrome can get in the way of meeting remarkable candidates, so please don’t hesitate to apply. We’d love to hear from you!
MRI is proud to be an inclusive employer. We welcome and celebrate diversity across all backgrounds, including ethnicity, religion, sexual orientation, gender identity, disability, age, military, veteran status and more.
We believe that Belonging is a direct result of Diversity, Equity, and Inclusion. Those values are woven into the fabric of who we are and are foundational to our continued success. Come and see for yourself!
Ideal Candidate
Someone who has scaled a cloud data platform (Snowflake/Databricks/Spark stack) from 3-5 years in a hands-on leadership role, ideally with exposure to multi-source domain modeling (financial services, SaaS, or marketplace platforms).
Estimated Salary Range(medium confidence)
₹40 L – ₹65 L per year
Likely Interview Questions
- 1.Walk us through a Common Data Model you've designed. How did you handle conflicting definitions of the same entity across product teams, and how did you measure adoption?
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