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 MicrosoftVSCode 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.
