Data Engineer

Job Description

We are seeking a highly experienced and self-driven Senior Data Engineer to design, build, and optimize modern data pipelines and infrastructure. This role requires deep expertise in Snowflake, DBT, Python, and cloud data ecosystems. You will play a critical role in enabling data-driven decision-making across the organization by ensuring the availability, quality, and integrity of data. Key Responsibilities: Design and implement robust, scalable, and efficient data pipelines using ETL/ELT frameworks. Develop and manage data models and data warehouse architecture within Snowflake . Create and maintain DBT models for transformation, lineage tracking, and documentation. Write modular, reusable, and optimized Python scripts for data ingestion, transformation, and automation. Collaborate closely with data analysts, data scientists, and business teams to gather and fulfill data requirements. Ensure data integrity, consistency, and governance across all stages of the data lifecycle. Monitor pipeline performance and implement optimization strategies for queries and storage. Follow best practices for data engineering including version control (Git), testing, and CI/CD integration. Required Skills and Qualifications: 8+ years of experience in Data Engineering or related roles. Deep expertise in Snowflake : schema design, performance tuning, security, and access controls. Proficiency in Python , particularly for scripting, data transformation, and workflow automation. Strong understanding of data modeling techniques (e.g., star/snowflake schema, normalization). Proven experience with DBT for building modular, tested, and documented data pipelines. Familiarity with ETL/ELT tools and orchestration platforms like Apache Airflow or Prefect . Advanced SQL skills with experience handling large and complex data sets. Exposure to cloud platforms such as AWS , Azure , or GCP and their data services. Preferred Qualifications: Experience implementing data quality checks and governance frameworks. Understanding of modern data stack and CI/CD pipelines for data workflows. Contributions to data engineering best practices, open-source projects, or thought leadership.