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The SouthState story is one of steady growth, deep community roots, and an unwavering commitment to helping our customers move forward. Since our beginnings in the 1930s to becoming a trusted financial partner across the South and beyond - we are known for combining personal relationships with forward-thinking solutions. We are committed to helping our team members find their success while maintaining the integrity of our values: building trust, fostering lasting relationships and pursuing excellence. At SouthState, individual contributions are recognized, potential is cultivated and team members are inspired to achieve their greater purpose. Your future begins here! Summary 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. Duties & 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. It is the responsibility of this role to take ownership of all tasks and challenges that they encounter in the operation of their assigned position. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Qualifications Education Requirements Minimum: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field, or equivalent combination of education and relevant professional experience. Preferred: Master’s degree in Computer Science, Data Engineering, Information Systems, Data Science, or related discipline. Minimum 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. 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. Licenses & Certifications 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). Preferred: dbt Certification or equivalent analytics engineering credential. Additional Physical Demands Ability to sit for extended periods and work extensively on a computer for sustained periods. Knowledge, Skills, & Abilities Data Engineering & Pipeline Orchestration 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 for analytics engineering: incremental models, snapshots, seeds, macros, tests, and multi-environment deployment strategies. Cloud & Data Platform Expertise Advanced hands-on experience with Snowflake, including schema design, role-based access patterns, warehouse sizing, query optimization, data sharing concepts, cost/performance tradeoffs, and integration with cloud storage and transformation tools. Strong proficiency with cloud-native data warehouses and lakehouses: Snowflake, AWS Redshift, Azure Synapse Analytics, or GCP BigQuery. Experience with lakehouse storage formats: Delta Lake, Apache Iceberg, or Apache Hudi. Working knowledge of modern cloud data architecture patterns across Azure, AWS, or GCP, with preference for experience integrating Snowflake into enterprise data ecosystems Proficiency with containerization and orchestration: Docker and Kubernetes for deploying data workloads. ML/AI & Advanced Analytics Integration Demonstrated understanding of data engineering patterns that support AI/ML and advanced analytics use cases, including semantic model development, reusable feature data, model-ready datasets, feature store design and management, and scalable analytical data products. Experience integrating AI-enabled workflows into data pipelines and analytics applications, including unstructured data processing, semantic search patterns, embedding-based approaches, and workflow orchestration. Knowledge of MLOps and production analytics practices, including model lifecycle support, workflow automation, environment promotion, monitoring frameworks, governance considerations, performance monitoring, drift awareness, and continuous improvement of data inputs and outputs Familiarity with orchestrating AL agent workflows and tool-calling patterns using frameworks such as MCP or similar technologies. Exposure to vector databases or retrieval-augmented generation (RAG) pipelines is preferred. Streaming & Real-Time Data Proficiency with real-time
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.