Seeking a skilled Application Support Engineer to act as a software detective within critical business systems. This role involves dynamic service delivery, identifying and resolving issues across multiple system components. You will ensure the smooth and uninterrupted operation of business processes through proactive problem-solving and detailed analysis.

This position requires a keen eye for detail and the ability to adapt to various system elements. Continuous engagement and a commitment to maintaining system integrity and performance are key. The engineer will play a crucial role in upholding the reliability of our technological infrastructure.

Provide comprehensive L2 and L3 support for DBT-based data transformation pipelines, ensuring swift resolution of incidents and service requests. Investigate and fix complex DBT model failures, data quality concerns, and performance bottlenecks in production environments. Conduct root cause analysis for critical incidents and implement preventative actions to preclude recurrence. Collaborate with data engineering and analytics teams to refine, optimize, and manage DBT models, macros, and packages. Monitor pipeline health and batch completion trends, proactively identifying operational stability risks. Manage and enhance incremental models, delta processing logic, and data vault integrations. Support platform migrations, schema modifications, and DBT version upgrades with minimal production disruption. Develop and maintain essential runbooks, technical documentation, and support SOPs for DBT workflows. Coordinate with L1 support teams for effective issue triage and escalation. Participate in on-call rotations to provide continuous 24/7 support for critical pipelines.

A minimum of 2-5 years of professional experience is required for this role, with a specific emphasis on data engineering, including at least 3 years of hands-on experience with Data Build Tool (DBT). Proficiency in SQL and experience with major cloud data warehouses like Snowflake, BigQuery, Redshift, or Databricks are essential. Solid understanding of data modeling methodologies such as Data Vault or Kimball is expected. Demonstrated experience in production support, incident management, and delivering within SLA constraints is necessary. Familiarity with orchestration tools like Apache Airflow or dbt Cloud, along with Python scripting for automation and troubleshooting, is beneficial. Experience with version control systems (Git) and CI/CD pipelines for DBT deployments, alongside strong analytical and problem-solving skills, are crucial. Excellent communication abilities are required to effectively engage with both technical and non-technical stakeholders. A foundational understanding of data governance and metadata management tools is advantageous. Knowledge of advanced DBT features, including custom macros and Jinja templating, is a plus. Completion of 15 years of full-time education is a prerequisite for this position.