IT Data Engineer IV

Southstate Bank•Va, NC

2d•Hybrid

About The Position

The IT Data Engineer IV serves as a senior technical lead for enterprise data engineering, responsible for designing, building, and improving scalable data platforms, Snowflake data solutions, and dbt-based transformation frameworks that support analytics, reporting, and operational decision-making across South State Bank. This role leads complex data engineering efforts, establishes standards for reliable and well-documented pipelines, and partners closely with business departments, data owners, architects, compliance, and technology teams to translate business needs into durable data solutions. The Data Engineer IV also mentors other engineers, drives continuous improvement in data quality and observability, and ensures data solutions align with enterprise architecture, governance, and regulatory expectations.

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, with at least 3 years in a senior or lead technical capacity.
5+ years of hands-on experience designing, developing, and optimizing solutions in Snowflake, including SQL development, data modeling, performance tuning, security/access patterns, and production support.
5+ years serving in a senior engineer, technical lead, solution lead, or architecture-influencing capacity.
5+ years of hands-on experience with dbt, including model development, testing, documentation, macros, environment promotion, and CI/CD integration.
5+ years of experience with SQL and Python for data engineering, automation, transformation, and data quality validation.
3+ years of experience with pipeline orchestration tools.
Experience partnering directly with business departments, data owners, reporting teams, architects, compliance, and other 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.
Ability to sit for extended periods and work extensively on a computer for sustained periods.

Nice To Haves

Master’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or related discipline.
Experience with streaming, lakehouse architecture, metadata management, data observability, or AI/ML data preparation is preferred.
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 is strongly preferred.
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 lakehouse storage formats: Delta Lake, Apache Iceberg, or Apache Hudi.
Exposure to vector databases or retrieval-augmented generation (RAG) pipelines is preferred.

Responsibilities

Architect, design, and deliver enterprise data platform solutions leveraging Snowflake, dbt, SQL, Python, and modern data integration technologies, ensuring scalable, secure, reliable, and maintainable data pipelines and analytical data products.
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 business requirements into data solutions, data models, integration patterns, and implementation roadmaps that deliver measurable business value.
Lead the support, monitoring, and continuous improvement of enterprise data platforms by resolving complex production issues, improving performance, enhancing data quality, and ensuring operational reliability.
Provide technical leadership, mentorship, and strategic direction for the data engineering practice, including architectural reviews, coaching engineers, evaluating emerging technologies, and supporting advanced analytics, AI/ML, and enterprise data initiatives.
Take ownership of all tasks and challenges that they encounter in the operation of their assigned position.