IT Data Engineer IV

South State Bank • Columbia, SC

65d • $87,282 - $139,425

About The Position

The IT Data Engineer IV serves as a senior technical lead for enterprise data engineering, designing, building, and improving scalable data platforms, Snowflake solutions, and dbt-based transformation frameworks that support analytics, reporting, and operational decision-making across South State Bank. The role leads complex data engineering efforts and establishes standards for reliable, well-documented pipelines. It works with business departments, data owners, architects, compliance, and technology teams to translate business needs into durable data solutions aligned with enterprise architecture, governance, and regulatory expectations.

The position also mentors engineers, promotes continuous improvement in data quality and observability, and supports advanced analytics, AI/ML, and broader enterprise data initiatives. It requires ownership of assigned tasks and challenges. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.

A bachelor’s degree in a relevant technical field or an equivalent combination of education and relevant professional experience is required; a master’s degree is preferred. The role requires 8–10+ years of progressive data engineering experience, including at least three years in a senior or lead technical capacity. The position’s stated anticipated pay range is $87,282–$139,425, with actual offers based on skills, experience, and education. The anticipated posting deadline is September 17, 2026.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field, or equivalent combination of education and relevant professional experience.
• 8–10+ years of progressive data engineering experience, including at least 3 years in a senior or lead technical capacity.
• 5+ years designing, developing, and optimizing Snowflake solutions, including SQL development, data modeling, performance tuning, security/access patterns, and production support.
• 5+ years serving as a senior engineer, technical lead, solution lead, or in an architecture-influencing capacity.
• 5+ years of hands-on dbt experience, including model development, testing, documentation, macros, environment promotion, and CI/CD integration.
• 5+ years using SQL and Python for data engineering, automation, transformation, and data quality validation.
• 3+ years of experience with pipeline orchestration tools.
• Experience partnering with business departments, data owners, reporting teams, architects, compliance, and technology groups to gather requirements, communicate design decisions, and deliver production data solutions.
• Experience supporting production data pipelines, troubleshooting data quality issues, conducting root cause analysis, and implementing monitoring or preventive controls.
• Demonstrated experience architecting ML/AI data infrastructure, including feature stores, MLOps pipelines, and LLM-based data processing workflows.
• Expert ability to design maintainable data pipelines using SQL, Python, Snowflake, dbt, and enterprise orchestration patterns.
• Strong understanding of production-grade engineering practices, including code reviews, version control, CI/CD, environment promotion, automated testing, documentation, monitoring, and incident response.
• Ability to troubleshoot complex pipeline, transformation, performance, and data quality issues across multiple systems; advanced command of dbt features and multi-environment deployment strategies.
• Advanced hands-on Snowflake experience, including schema design, role-based access, warehouse sizing, query optimization, data sharing, cost/performance tradeoffs, and cloud storage and transformation integrations.
• Strong proficiency with cloud-native data warehouses and lakehouses, and working knowledge of cloud data architecture patterns across Azure, AWS, or GCP.
• Proficiency with Docker and Kubernetes for deploying data workloads.
• Demonstrated understanding of data engineering patterns supporting AI/ML and advanced analytics, including semantic models, feature data, model-ready datasets, feature stores, and analytical data products.
• Experience integrating AI-enabled workflows into data pipelines and analytics applications, including unstructured data processing, semantic search, embedding-based approaches, and workflow orchestration.
• Knowledge of MLOps and production analytics practices, including model lifecycle support, automation, promotion, monitoring, governance, performance monitoring, and drift awareness.
• Familiarity with orchestrating AI agent workflows and tool-calling patterns using MCP or similar technologies.
• Proficiency with real-time and event-driven data platform concepts, including message streaming, event ingestion, pub/sub patterns, and enterprise platform integration.
• Experience designing or supporting stream processing for near-real-time data transformation, enrichment, aggregation, and delivery; knowledge of change data capture and incremental data movement patterns.
• Strong understanding of data quality frameworks, automated data observability and lineage tracking, metadata management, data cataloging, and data classification practices in regulated environments.
• Ability to design scalable, cost-optimized data architectures using medallion, data vault, or dimensional modeling patterns.
• Strong technical communication, documentation, facilitation, analytical, organizational, and stakeholder-influence skills; ability to mentor engineers, lead code and design reviews, and drive engineering best practices.
• Demonstrated initiative and ownership in ambiguous, fast-paced environments.
• Ability to communicate in person, by phone, and electronically; use a computer frequently; sit, walk, and/or stand for extended periods; and bend and reach.

Nice To Haves

• Master’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or a related discipline.
• Experience with streaming, lakehouse architecture, metadata management, data observability, or AI/ML data preparation.
• Prior experience in financial services, banking, or another regulated industry.
• Cloud data platform certification such as AWS Certified Data Analytics – Specialty, Microsoft Certified: Azure Data Engineer Associate, GCP Professional Data Engineer, or Snowflake SnowPro Certifications (Core, Associate, Advanced).
• dbt Certification or equivalent analytics engineering credential.
• Experience with Delta Lake, Apache Iceberg, or Apache Hudi.
• Experience with Atlan or Monte Carlo for data observability and lineage tracking.
• Experience with Collibra, Alation, or Apache Atlas for data cataloging.
• Exposure to vector databases or retrieval-augmented generation (RAG) pipelines.
• Familiarity with data mesh and data product ownership principles.

Responsibilities

• Architect, design, and deliver enterprise data platform solutions using Snowflake, dbt, SQL, Python, and modern data integration technologies.
• Establish and enforce data engineering standards, best practices, and governance frameworks for code quality, CI/CD, data testing, observability, documentation, metadata management, security, and operational excellence.
• Partner with business stakeholders, data owners, architects, compliance teams, and technology partners to translate requirements into data solutions, models, integration patterns, and implementation roadmaps.
• Support, monitor, and continuously improve enterprise data platforms by resolving complex production issues, improving performance and data quality, and ensuring operational reliability.
• Provide technical leadership, mentorship, and strategic direction for the data engineering practice, including architectural reviews, coaching, technology evaluation, and support for advanced analytics, AI/ML, and enterprise data initiatives.
• Take ownership of tasks and challenges encountered in operating the assigned position.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior