Lead Support Engineer
Charlotte, NC or Remote EST
$65-$70/hour W2
6 Month Contract to Hire

Our global Fortune 500 client, with U.S. headquarters in Charlotte, NC, is a world class food service provider with a strong presence across the nation. Celebrating almost 30 years in North America, this employee-focused company has received honors for diversity and inclusion, innovation, health and wellness, and company culture. CRG has successfully placed over 220 employees within the last 7 years within this organization, known for its continuous growth opportunities, fantastic benefits package, innovative technology, flexible work environment, and collaborative culture.

About the Role
The Lead Application Support Engineer plays a key role in maintaining the stability, performance, and reliability of our enterprise data platform. This role requires strong hands-on expertise across modern data stack technologies including Snowflake, Airflow, dbt, Fivetran, Python, and AWS Lambda, along with deep experience in incident management and production support. You will be responsible for owning and resolving high-severity incidents, ensuring SLAs are met, and proactively identifying improvements in data workflows and automation. The ideal candidate can troubleshoot complex data ingestion and transformation pipelines, collaborate with development teams, and ensure end-to-end operational excellence in a production environment.

Responsibilities

• Own resolution of high-severity and complex incidents escalated from L2.
• Translate recurring incidents into actionable backlog items; ensure linkage between incidents, bugs, and stories.
• Partner with Development teams to validate bug fixes and story completions in lower environments.
• Provide visibility into backlog health, ensuring business-critical items are prioritized.
• Investigating Snowflake table/view refresh failures and analyzing logs.
• Checking and re-running Airflow DAGs or dbt models as part of recovery procedures.
• Validating ingestion via Fivetran and confirming successful data loads using SQL.
• Reviewing ETL/ELT job dependencies and manually triggering failed runs.
• Identifying and resolving timing or sequencing issues in scheduled data jobs.
• Maintain and enhance runbooks, SOPs, and knowledge base documentation.
• Participate in release readiness activities, deployments, and post-release validations.
• Mentor junior support engineers on best practices for troubleshooting and incident management.
• Advocate for customer and business impact during sprint planning and prioritization.

Qualifications

• 5+ years of experience in application support, production support, or data platform operations.
• Proven ability to troubleshoot data-platform workflows independently.
• Strong analytical and Root Cause Analysis (RCA) skills for complex production incidents.
• Proficiency in Azure DevOps (ADO) for backlog and release management.
• Hands-on experience with monitoring tools (Splunk, Dynatrace, Zabbix, AlertBot).
• Knowledge of ITIL practices (Incident, Problem, Change)
• Excellent communication skills and ability to collaborate with cross-functional teams.

• Bachelor’s degree in Computer Science, Information Technology, or related field, or equivalent experience.
• Certifications such as ITIL Intermediate/Expert, Splunk Power User, Dynatrace Associate, or Certified Problem Manage.

Core Technical Skills (Required)

• Snowflake: Querying, performance monitoring, task scheduling, data validation, query optimization.
• SQL: Strong ability to write, debug, and optimize complex queries.
• Airflow: Managing DAG dependencies, task retries, scheduling, and manual triggers.
• dbt: Running and debugging models, understanding project structure, validating transformation errors.
• Fivetran: Monitoring connectors, reviewing logs, performing manual refreshes, validating data loads.
• Python: Reading and modifying ETL or Lambda scripts, understanding event-driven code flows.
• AWS Lambda: Reviewing logs, debugging execution results, validating event-based triggers.
• ETL/ELT: Strong understanding of ingestion and transformation flows across the data lifecycle.