IT Data Engineer IV

SouthState Bank • Winter Haven, SC

17h • $87,282 - $139,425

About The Position

The IT Data Engineer IV serves as a senior technical lead for enterprise data engineering at South State Bank. The role designs, builds, and improves scalable data platforms, Snowflake solutions, and dbt-based transformation frameworks that support analytics, reporting, and operational decision-making. It leads complex engineering efforts, establishes standards for reliable and well-documented pipelines, and partners with business departments, data owners, architects, compliance, and technology teams to turn business needs into durable data solutions. The position also mentors engineers, advances data quality and observability, and ensures solutions align with enterprise architecture, governance, and regulatory expectations.

The role provides technical leadership for enterprise data platforms and engineering practices, including architecture, implementation, production support, monitoring, and continuous improvement. It also supports advanced analytics, AI/ML, and other enterprise data initiatives.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field, or an 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 of hands-on experience 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 of experience using SQL and Python for data engineering, automation, transformation, and data quality validation.
• 3+ years of experience with pipeline orchestration tools.
• Experience working directly 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.
• Expert-level 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, including incremental models, snapshots, seeds, macros, tests, 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 integration with cloud storage and transformation tools.
• Strong proficiency with cloud-native data warehouses and lakehouses such as Snowflake, AWS Redshift, Azure Synapse Analytics, or GCP BigQuery.
• Working knowledge of cloud data architecture patterns across Azure, AWS, or GCP.
• Proficiency with Docker and Kubernetes for deploying data workloads.
• Understanding of data engineering patterns supporting AI/ML and advanced analytics, including semantic models, reusable feature data, model-ready datasets, feature store design and management, 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, workflow automation, environment promotion, monitoring, governance, performance monitoring, drift awareness, and continuous improvement.
• 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 integration with enterprise data platforms.
• Experience designing or supporting stream processing for near-real-time transformation, enrichment, aggregation, and data delivery.
• Knowledge of change data capture and incremental data movement patterns.
• Strong understanding of data quality frameworks, automated data observability, and lineage tracking.
• Experience with metadata management, data cataloging, and data classification practices in regulated environments.
• Familiarity with data mesh and data product ownership principles.
• Ability to design scalable, cost-optimized data architectures using medallion, data vault, or dimensional modeling patterns.
• Strong technical communication skills, including the ability to produce architecture decision records and design documents and explain technical concepts to technical and non-technical audiences.
• Ability to facilitate requirements gathering and design sessions with business stakeholders and technical teams.
• Ability to influence cross-functional teams and drive consensus on enterprise data initiatives.
• Ability to mentor engineers, lead code and design reviews, and promote engineering best practices.
• Strong analytical, organizational, written, and verbal communication skills, including the ability to facilitate workshops, present recommendations, document decisions, communicate project status, and influence stakeholders.
• Demonstrated initiative and ownership in ambiguous, fast-paced environments.
• Ability to communicate in person, by phone, and through electronic channels; use a computer frequently; sit, walk, and/or stand for extended periods; and bend and reach.
• Ability to sit for extended periods and work extensively on a computer.

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.
• Demonstrated experience architecting ML/AI data infrastructure, including feature stores, MLOps pipelines, and LLM-based data processing workflows.
• 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 an equivalent analytics engineering credential.
• Experience with lakehouse storage formats such as Delta Lake, Apache Iceberg, or Apache Hudi.
• Experience with data quality and observability tools such as Atlan or Monte Carlo.
• Experience with data catalogs such as Collibra, Alation, or Apache Atlas.
• Exposure to vector databases or retrieval-augmented generation (RAG) pipelines.

Responsibilities

• Architect, design, and deliver enterprise data platform solutions using Snowflake, dbt, SQL, Python, and modern data integration technologies.
• Ensure data pipelines and analytical data products are scalable, secure, reliable, and maintainable.
• Establish and enforce data engineering standards, best practices, and governance frameworks for code quality, CI/CD, testing, observability, documentation, metadata management, security, and operational excellence.
• Partner with business stakeholders, data owners, architects, compliance teams, and technology partners to translate business requirements into data solutions, models, integration patterns, and implementation roadmaps.
• Lead support, monitoring, and continuous improvement of enterprise data platforms, including 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.
• Conduct architectural reviews, coach engineers, evaluate emerging technologies, and support advanced analytics, AI/ML, and enterprise data initiatives.
• Take ownership of tasks and challenges encountered in the operation of the assigned position.

What This Job Offers

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