Senior/Lead Data Engineer - Snowflake & Kafka

About 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.

Srijan, a 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. Be a part of an Awesome Tribe

Role- Lead Data Engineer (Azure Data Engineering, Snowflake, Data warehousing ,Kafka – Optional)

Job Responsibilities

We are seeking a Lead Data Engineer to design and deliver scalable, high-performance data platforms for real-time and batch analytics. The ideal candidate has deep expertise in data engineering, data modelling & warehousing, Snowflake, and Azure services, with proven ability to build, orchestrate, and optimise data pipelines end-to-end.

Azure Data Engineering, Pipelines & Processing (Datawarehouse)

• Architect, design, and build scalable batch and real-time data pipelines using Azure Data Engineering services (ADF, Synapse, Data Lake, Event Hub, Functions) and PySpark.
• Apply orchestration and load optimisation strategies for reliable, high-throughput pipelines.
• Implement both streaming (low-latency) and batch (high-volume) processing solutions.
• Drive best practices in data modelling, data warehousing, and SQL development.

Snowflake Cloud Data Warehouse

• Design and optimise data ingestion pipelines from multiple sources into Snowflake, ensuring availability, scalability, and cost efficiency.
• Implement ELT/ETL patterns, partitioning, clustering, and performance tuning for large datasets.
• Develop and maintain data models and data warehouses leveraging Snowflake-specific features (streams, tasks, warehouses).

Real-Time Streaming (Kafka – Optional)

• Design and implement event-driven architectures using Kafka (topic design, partitioning, consumer groups, schema management, monitoring).
• Ensure high-throughput, low-latency stream processing and data reliability.

Collaboration & Leadership

• Partner with data scientists, ML engineers, and business stakeholders to deliver high-quality, trusted datasets.
• Translate business requirements into scalable, reusable data engineering solutions.
• Provide technical leadership, mentoring, and knowledge-sharing within the team.

Required Skills & Qualifications

• 5+ years of Data Engineering experience in large-scale enterprise projects.
• Strong expertise in Snowflake: ELT/ETL pipelines, performance tuning, query optimisation, advanced features (streams, tasks, warehouses).
• Hands-on with Azure Data Engineering stack: ADF, Event Hub, Synapse, Databricks, Data Lake, Functions, scaling/load balancing strategies.
• Advanced SQL skills with proven ability to optimise transformations at scale.
• Proficiency in Python & PySpark for distributed, high-performance data processing.
• Demonstrated success delivering real-time and batch pipelines in cloud environments.

Preferred Skills

• CI/CD, Docker, DevOps, and server management.
• Monitoring with Azure Monitor, Log Analytics.
• Kafka (preferred but optional).