Senior Data Engineer

About the Role We are seeking a highly experienced and strategic Sr Data Engineer for the development and optimization of our modern cloud data platform infrastructure. This role is ideal for someone who thrives in a fast-paced environment, is passionate about data architecture, and has a deep understanding of data transformation, modeling, and orchestration using modern tools like dbt-core , Snowflake , and Python . Key Responsibilities Design and implement scalable data pipelines using dbt-core, Python, and SQL to support analytics, reporting, and data science initiatives. Design and optimize data models in Snowflake to support efficient querying and storage. Development and maintenance of our data warehouse , ensuring data quality, governance, and performance. Collaborate with cross-functional teams including data analysts, data architects, data scientists, and business stakeholders to understand data needs and deliver robust solutions. Establish and enforce best practices for version control (Git), CI/CD pipelines, and data pipeline monitoring. Mentor and guide junior data engineers , fostering a culture of technical excellence and continuous improvement. Evaluate and recommend new tools and technologies to enhance the data platform. Provide on-going support for the existing ELT/ETL processes and procedures. Identify tools and technologies to be used in the project as well as reusable objects that could be customized for the project Coding Required Qualifications Bachelor's degree in computer science or related field (16 years of formal education related to engineering) 6+ years of experience in data engineering or a related field. Expert-level proficiency in SQL and Python for data transformation and automation. Experience with dbt-core for data modeling and transformation. Strong hands-on experience in cloud platforms ( Microsoft Azure) and cloud data platforms ( Snowflake ). Proficiency with Git and collaborative development workflows. Familiarity with Microsoft VSCode or similar IDEs. Knowledge of Azure DevOps or Gitlab development operations and job scheduling tools. Solid understanding of modern data warehousing architecture , dimensional modeling, ELT/ETL frameworks and data modeling techniques. Excellent communication skills and the ability to translate complex technical concepts to non-technical stakeholders. Proven expertise in designing and implementing batch and streaming data pipelines to support near real-time and large-scale data processing needs. Preferred Qualifications Experience working in a cloud-native environment (AWS, Azure, or GCP). Familiarity with data governance, security, and compliance standards. Prior experience with Apache Kafka (Confluent). Artificial Intelligence (AI) experience is a plus. Hands-on experience with orchestration tools (e.g., Airflow, Prefect) is a plus.