IT Data Engineer IV

This is a fully remote position, open to applicants in Alabama, +6 more states.

📋 Description

• Design, architect, and implement enterprise data platform solutions utilizing Snowflake, dbt, SQL, Python, and contemporary data integration technologies.

• Develop and uphold data engineering standards, best practices, and governance to ensure code quality, CI/CD, testing, observability, documentation, metadata management, security, and operational excellence.

• Collaborate with business stakeholders, data owners, architects, compliance teams, and technology partners to convert requirements into data solutions, models, integration patterns, and strategic roadmaps.

• Oversee the support, monitoring, and ongoing enhancement of enterprise data platforms.

• Troubleshoot complex production issues, enhance performance and data quality, and guarantee operational reliability.

• Offer technical leadership, mentorship, architectural reviews, coaching, technology assessment, and strategic guidance for the data engineering practice.

• Facilitate advanced analytics, AI/ML, and enterprise data initiatives.

• Take responsibility for tasks and challenges encountered in the designated role.

⛳️ Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related technical discipline, or an equivalent combination of education and relevant professional experience.

• 8–10+ years of progressive experience in data engineering, including a minimum of 3 years in a senior or lead technical role.

• 5+ years of experience in designing, developing, and optimizing solutions using Snowflake.

• 5+ years in a senior engineer, technical lead, solution lead, or architecture-influencing position.

• 5+ years of hands-on experience with dbt.

• 5+ years of expertise in SQL and Python.

• 3+ years of experience with pipeline orchestration tools.

• Proven experience collaborating with business departments, data owners, reporting teams, architects, compliance personnel, and technology groups.

• Experience maintaining production data pipelines, addressing data quality issues, conducting root cause analysis, and implementing monitoring or preventive controls.

• Demonstrated expertise in architecting ML/AI data infrastructure, including feature stores, MLOps pipelines, and LLM-based data processing workflows.

• Familiarity with streaming, lakehouse architecture, metadata management, data observability, or AI/ML data preparation is preferred.

• Prior experience in financial services, banking, or another regulated industry is strongly preferred.

• 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.

• Preferred dbt Certification or an equivalent analytics engineering credential.

• Expert-level proficiency in SQL, Python, Snowflake, dbt, and enterprise orchestration patterns.

• Strong production-grade engineering practices, including code reviews, version control, CI/CD, automated testing, documentation, monitoring, and incident response.

• Advanced knowledge of dbt, including incremental models, snapshots, seeds, macros, tests, and multi-environment deployment strategies.

• Extensive hands-on expertise with Snowflake, covering schema design, access patterns, warehouse sizing, query optimization, data sharing, and cloud integrations.

• Proficiency with Snowflake, AWS Redshift, Azure Synapse Analytics, or GCP BigQuery.

• Experience with Delta Lake, Apache Iceberg, or Apache Hudi.

• Familiarity with Azure, AWS, or GCP cloud data architecture.

• Proficiency in Docker and Kubernetes.

• Understanding of AI/ML data engineering, semantic models, feature data, feature stores, model-ready datasets, and analytical data products.

• Experience with unstructured data processing, semantic search, embeddings, and workflow orchestration.

• Knowledge of MLOps, model lifecycle support, monitoring, governance, drift awareness, and continuous improvement.

• Familiarity with orchestrating AI agent workflows and tool-calling patterns using MCP or similar technologies.

• Proficiency in real-time and event-driven data platforms, message streaming, event ingestion, pub/sub, and enterprise integrations.

• Experience with stream processing, change data capture, and incremental data movement.

• Strong understanding of data quality frameworks, observability, lineage tracking, metadata management, data cataloging, and data classification.

• Ability to design scalable, cost-efficient architectures using medallion, data vault, or dimensional modeling methodologies.

• Excellent technical communication, analytical, facilitation, mentoring, and stakeholder-influence skills.

• Ability to sit for extended periods and work extensively on a computer.

🏝️ Benefits

• Reasonable accommodations may be made to assist individuals with disabilities in performing essential functions.

• A secure and distraction-free remote or hybrid work environment is required, with a reliable internet connection (cable or fiber preferred).

• Equal Opportunity Employer, including individuals with disabilities and veterans.