ETL Team Lead

Canal Insurance Company•Greenville, SC

30d•Onsite

About The Position

Canal Insurance Company is seeking an ETL Team Lead to own operational support and technical leadership for its existing data stack while guiding modernization of the company’s data operations layer. The role supports reliable daily ETL processing, reporting SLAs, production releases, and critical financial reporting cycles, while collaborating with AMS, IT operations, business stakeholders, and technical teams. The ETL Team Lead will maintain and improve pipelines across SQL Server, SSIS, Guidewire DataHub and InfoCenter, Oracle, SAP BODS, PostgreSQL, and PowerShell. The role also leads development of reusable ingestion patterns and scalable ETL/ELT pipelines, and helps modernize workloads using Azure data technologies, Delta Lake, Medallion Architecture, and Fabric Lakehouse. Responsibilities include real-time and event-driven data engineering, data modeling and curation, data governance support, and establishing data quality and observability practices. Preparing ML-ready datasets is optional.

Nice To Haves

Experience preparing ML-ready datasets for pricing, risk, fraud detection, underwriting, claims leakage, and predictive insights (optional).

Responsibilities

Own end-to-end operational support for Canal’s existing data stack, including monitoring daily ETL loads across SQL jobs, DHIC (GW DataHub and InfoCenter), and legacy SSIS packages.
Troubleshoot pipeline failures, performance issues, schema mismatches, permissions issues, and cloud resource failures with the AMS team; perform root-cause analysis and implement permanent fixes.
Ensure SLA adherence and on-time delivery of critical reporting datasets for scheduled ETL jobs.
Provide direction to AMS and ETL developers for legacy and current ETL maintenance, and refactor or retire outdated or redundant ETL processes.
Maintain and improve pipelines using SQL Server, T-SQL, SSIS, PowerShell, Guidewire DataHub and InfoCenter, Oracle, PL/SQL, SAP BODS, and PostgreSQL.
Work with AMS to create or enhance operational runbooks, SOPs, monitoring dashboards, and incident response workflows.
Partner with IT operations, business SMEs, and AMS to minimize downtime and meet business SLAs; improve existing and implement new proactive measures for daily processing.
Coordinate rotation-based development support coverage for critical data pipelines, support month-end and quarter-end financial reporting, and coordinate production releases and deployment validation.
Become the steady-state technical owner of the data operations layer during Canal’s modernization journey.
Guide onshore and offshore developers, review code, enforce best practices, and mentor junior engineers.
Partner with Scrum Masters, project managers, Enterprise Architecture, QA Automation, Change Management, and AMS support teams.
Develop reusable ingestion patterns for Guidewire DataHub and InfoCenter, HubSpot, telematics, and other business areas; integrate internal and external data into the platform.
Work with Canal architects to modernize ETL workloads using Delta Lake, Medallion Architecture, and Fabric Lakehouse; build scalable ingestion pipelines using potential technologies such as Azure Data Factory, Microsoft Fabric, Databricks, and Synapse Pipelines.
Design and implement real-time data pipelines using Event Hub, Fabric Real-Time Analytics, Databricks Structured Streaming, and KQL-based event processing.
Enable real-time operational insights and automation, including telematics alerting, FNOL automation, and fraud/VNOS/VNOP detection through event-driven architectures and streaming analytics.
Lead strategy, design, and engineering for Canal’s modern Azure data ecosystem, including implementing Bronze/Silver/Gold Medallion Architecture across Fabric Lakehouse, Warehouse, Eventhouse, and KQL Database, and leveraging Delta tables with schema enforcement, ACID compliance, and versioning.
Develop curated, analytics-ready datasets for Power BI, operational reporting, and advanced analytics; assist the Canal architect with data governance tool implementation.
Establish data quality, validation, alerting, and observability frameworks to maintain enterprise data accuracy, consistency, and trust.
Optionally prepare ML-ready datasets for pricing, risk, fraud detection, underwriting, claims leakage, and predictive insights.