Senior/Lead Data Engineer - Snowflake & Azure

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.

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).