Senior Data Engineer & Technical Lead

As a Senior Data Engineer/Lead, you will be responsible for the following: - Designing and developing scalable batch and streaming data pipelines using PySpark and Databricks - Building and optimizing enterprise data models and transformation pipelines using Snowflake and DBT - Developing advanced SQL scripts, stored procedures, and performance-optimized queries - Designing and implementing real-time streaming architectures - Building integrations using APIs, event-driven architectures, and cloud-native services - Optimizing workloads for performance, scalability, and cost efficiency - Designing enterprise data platforms using Lakehouse, Data Mesh, Data Vault, and Fabric principles - Working with cross-functional teams to build scalable analytics and data engineering solutions - Implementing CI/CD and DevOps practices for data engineering pipelines - Providing technical leadership and mentoring engineering teams As a qualified candidate, you should possess the following skills: - Strong hands-on experience with PySpark - Expertise in Databricks - Strong Snowflake experience - Strong DBT knowledge - Advanced SQL expertise - Strong experience with Stored Procedures and complex SQL optimization - Hands-on experience with streaming frameworks and real-time processing - Experience with REST APIs and enterprise integrations - Strong understanding of distributed data processing - Working knowledge of AWS and/or Azure - Excellent analytical and debugging skills You should have strong knowledge in: - Data Warehousing - Data Vault Modeling - Data Mesh - Data Lake & Lakehouse Architecture - Microsoft Fabric - Enterprise Data Architecture Patterns Good to have skills include: - Kafka/Event Hub/Kinesis - Airflow - Terraform - Unity Catalog - Delta Live Tables - CI/CD pipelines As a Senior Data Engineer/Lead, you will be responsible for the following: - Designing and developing scalable batch and streaming data pipelines using PySpark and Databricks - Building and optimizing enterprise data models and transformation pipelines using Snowflake and DBT - Developing advanced SQL scripts, stored procedures, and performance-optimized queries - Designing and implementing real-time streaming architectures - Building integrations using APIs, event-driven architectures, and cloud-native services - Optimizing workloads for performance, scalability, and cost efficiency - Designing enterprise data platforms using Lakehouse, Data Mesh, Data Vault, and Fabric principles - Working with cross-functional teams to build scalable analytics and data engineering solutions - Implementing CI/CD and DevOps practices for data engineering pipelines - Providing technical leadership and mentoring engineering teams As a qualified candidate, you should possess the following skills: - Strong hands-on experience with PySpark - Expertise in Databricks - Strong Snowflake experience - Strong DBT knowledge - Advanced SQL expertise - Strong experience with Stored Procedures and complex SQL optimization - Hands-on experience with streaming frameworks and real-time processing - Experience with REST APIs and enterprise integrations - Strong understanding of distributed data processing - Working knowledge of AWS and/or Azure - Excellent analytical and debugging skills You should have strong knowledge in: - Data Warehousing - Data Vault Modeling - Data Mesh - Data Lake & Lakehouse Architecture - Microsoft Fabric - Enterprise Data Architecture Patterns Good to have skills include: - Kafka/Event Hub/Kinesis - Airflow - Terraform - Unity Catalog - Delta Live Tables - CI/CD pipelines