Sr Data Engineer/ Tech Data Leader We are looking for a highly skilled Senior Data Engineer with deep hands-on experience in modern data architecture and tools, especially Snowflake, Apache Iceberg, and DBT. As a senior member of our data engineering team, you will play a critical role in designing, building, and maintaining our data infrastructure, pipelines, and modeling layer to support business intelligence, advanced analytics, and data science initiatives. Key Responsibilities • Design, implement, and optimize scalable and reliable data pipelines using Snowflake and DBT. • Lead the adoption and integration of Apache Iceberg for data lakehouse architecture and large-scale data processing. • Collaborate with data analysts, data scientists, and stakeholders to define data requirements and deliver high-quality data models and transformations. • Build and maintain batch and streaming data ingestion workflows using best practices for performance, reliability, and scalability. • Define and enforce data governance, lineage, quality, and documentation standards. • Mentor junior data engineers and contribute to setting engineering best practices. • Troubleshoot data pipeline issues and provide support for data-related operational challenges. Required Qualifications • 10+ years of professional experience in data engineering or a related role. • Strong expertise with Snowflake (warehousing, performance tuning, Snowpipe, data sharing, etc.). • Proven experience working with Apache Iceberg or similar table formats (e.g., Delta Lake, Hudi). • Deep understanding and practical use of the DBT stack (models, tests, snapshots, macros). • Strong proficiency in SQL and at least one programming language (e.g., Python or Scala). • Solid understanding of data architecture principles, dimensional modeling, and ELT best practices. • Experience with cloud platforms (e.g., AWS, GCP, Azure) and services like S3, EMR, Lambda, etc. • Familiarity with CI/CD practices and tools for data pipeline deployments. Preferred Qualifications • Experience with orchestration tools (e.g., Airflow, Prefect). • Knowledge of data lake and lakehouse architecture patterns. • Familiarity with version control systems (e.g., Git) and DevOps principles. • Exposure to data observability and monitoring tools (e.g., Monte Carlo, Datafold). • Prior experience in a regulated or data-sensitive industry (e.g., insurance, finance, healthcare) is a plus.