India onlyAbout Us
We turn customer challenges into growth opportunities. Material is a global strategy partner to the world’s most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences. We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve. Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners.
Role Summary- We are looking for a Lead Data Engineer to design, build, and scale the or Customer data engineering platforms. This role is hands-on and requires deep expertise in Python-based data engineering with Pyspark and use of Microsoft Fabric ecosystem. The candidate will work on building analytics-heavy workflows, converting business logic and analytical models into production-grade pipelines, and enabling enterprise-scale insights.
Key Responsibilities
- Design, develop, and maintain scalable Python-based data integration pipelines.
- Pandas, Numpy, Pyarrow, Polars, Dask etc.
- expertise with Pyspark !
- Build and optimize data workflows using Microsoft Fabric (Lakehouse, Pipelines, Notebooks, Dataflows Gen2).
- Convert complex business logic, Excel models, and analytical workflows into Python/Pyspark pipelines.
- Implement high-performance ETL / ELT pipelines for large-scale analytics.
- Ensure data quality, validation, reconciliation, and monitoring.
- Design robust data models and semantic layers for analytics and BI.
- Collaborate with analytics, BI, and product teams to deliver end-to-end data solutions.
- Mentor engineers and contribute to platform and team scaling.
Required Skills & Experience - 5+ years of experience in Data Engineering, with strong hands-on Python / Pyspark skills.
- Proven ability to translate Excel based mapping spreadsheets / business calculations into Python / Pyspark data engineering workflows.
- Experience working in a Lead / Senior Data Engineer capacity.
- Experience with Microsoft Fabric ecosystem. (Good to have)
- Strong SQL, data integration, and performance tuning skills.
- Experience with large-scale analytical data platforms (Databricks / Fabric / Synapse ).
- Ability to work independently with minimal supervision.
Who We Are & What We Care About
· Material is a global company and we work with best-of-class brands worldwide. We also create and launch new brands and products, putting innovation and value creation at the center of our practice. Our clients are in the top of their class, across industry sectors from technology to retail, transportation, finance and healthcare.
· Material employees join a peer group of exceptionally talented colleagues across the company, the country, and even the world. We develop capabilities, craft and leading-edge market offerings across seven global practices including strategy and insights, design, data & analytics, technology and tracking. Our engagement management team makes it all hum for clients.
· We prize inclusion and interconnectedness. We amplify our impact through the people, perspectives, and expertise we engage in our work. Our commitment to deep human understanding combined with a science & systems approach uniquely equips us to bring a rich frame of reference to our work.
· A community focused on learning and making an impact. Material is an outcomes focused company. We create experiences that matter, create new value and make a difference in people's lives.
What We Offer
· Professional Development and Mentorship.
· Hybrid work mode with remote friendly workplace. (6 times in a row Great Place To Work Certified).
· Health and Family Insurance.
· 40+ Leaves per year along with maternity & paternity leaves.
· Wellness, meditation and Counselling sessions.
United States
