Overview

We are seeking a Data Support Engineer to serve as the key operational link between our production data architecture and our business clients/stakeholders.

Responsibilities

• ETL Build Monitoring & Incident Management
• Pipeline Monitoring: Actively monitor nightly and intraday dbt build executions, dbt test assertions, and freshness checks across interconnected dbt Mesh projects.
• Build Failures & Recovery: Diagnose and resolve failed ETL/dbt runs (e.g., upstream source delays, contract breaches, timeout errors, connection drops) to restore pipeline operations within target SLAs.
• Runaway / Blocked Jobs: Track orchestration platform runs (Airflow, Dagster, GitHub Actions, or Snowflake Tasks) to re-trigger failed job steps or clear blocked dependencies.
• Client-Facing Ticket Management & Communication
• Ticket Lifecycle: Own the queue of client-facing tickets (via Jira, ServiceNow, Zendesk, or Slack/Teams) from initial triage to final resolution.
• Stakeholder SLA Management: Provide regular, clear, and proactive status updates to internal business teams, client analysts, and external partners during operational outages or data delay incidents.
• Root Cause Analysis (RCA) Communication: Draft clear, client-friendly summary reports explaining data issues, business impacts, and remedial actions taken.
• Access Management & user support
• Execute user and service account access requests across Snowflake databases, schemas, and dbt models adhering to Role-Based Access Control (RBAC ) guidelines.
• Troubleshoot client access issues (e.g., missing database grants, functional role mismatches, or row-access/masking policy restrictions) without compromising data security.
• Compute Cost & Warehouse Safeguards
• Monitor active Snowflake Virtual Warehouses and identify long-running queries, stuck sessions, or un-suspended resources.
• Safeguard credits by terminating rogue client queries or runaway build tasks and reporting compute spikes to management.
• Basic Query Debugging & Escalation
• Inspect failed or lagging queries using Snowflake’s Query Profile to identify basic performance bottlenecks (e.g., heavy spilling, massive table scans).
• Perform basic query fixes, adjustments to warehouse sizing, or manual data re-executes.
• Escalate structural dbt code issues, missing indexes/cluster keys, or schema changes to L3 Data Engineers with clear context and logs.

Qualifications

• Client-Facing & Communication: Excellent written and verbal communication skills; comfortable explaining technical data issues to non-technical business stakeholders.
• Ticketing Systems: Hands-on experience working with ticketing tools (e.g., Jira, ServiceNow, Zendesk) and managing operational SLAs.
• Snowflake Experience: 2+ years navigating Snowflake (Snowsight, Query History, Account Usage views, Role/Grant administration, Warehouse monitoring).
• dbt Core Experience: Knowledge of dbt CLI commands , dbt errors, and multi-project dependency handling.
• SQL & Troubleshooting: Solid SQL skills (CTEs, JOINs, window functions, aggregation debugging) to inspect data discrepancies and build failure points.
• Orchestration Familiarity: Basic experience with tools like Airflow, Snowflake Tasks, Dragster or GitHub Actions,.
• First Response & Resolution SLA: Time to acknowledge and resolve incoming client tickets within agreed timelines.
• ETL Uptime & Data Freshness: Percentage of morning build jobs completed before business client start times.
• Client Satisfaction (CSAT): Positive feedback on ticket communication, transparency, and problem resolution clarity.