Data Engineer - Snowflake/DBT SME (Remote)

TM Floyd & Company•Davenport, FL

1d•Remote

About The Position

TM Floyd & Company is hiring a remote Data Engineer with senior-level Snowflake and dbt expertise. The role focuses on designing scalable, secure, reliable, and maintainable enterprise data platform solutions and analytical data products. The successful candidate will bring strong data modeling and semantic layer experience, build AI-ready datasets, communicate effectively with business stakeholders, and provide technical guidance to other engineers. Experience with Snowflake Cortex, AI/ML, and modern analytics engineering practices is a plus. Candidates must be U.S. citizens or Green Card holders, able to work on the company’s W2; sponsorship is unavailable, and C2C or third-party candidates are not accepted. The position requires 8–10 years of progressive data engineering experience, including at least three years in a senior or lead technical capacity. Candidates should have five years of experience designing and optimizing Snowflake solutions and working hands-on with dbt, alongside SQL and Python skills. Experience with pipeline orchestration, semantic layer development, and collaboration with data science teams is also required. A bachelor's degree in a relevant technical field or an equivalent combination of education and professional experience is required. A master's degree, relevant cloud or Snowflake certifications, and a dbt certification are preferred. The engineer will establish engineering standards and governance, translate business needs into data solutions, support and improve enterprise data platforms, and provide technical leadership and mentorship. The employer offers benefits depending on assignment length and a referral bonus of up to $1,000.

Requirements

8–10 years of progressive data engineering experience, with at least 3 years in a senior or lead technical capacity.
5 years of experience designing, developing, and optimizing Snowflake solutions, including SQL development, data modeling, performance tuning, security and 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 dbt experience, including data products, model development, testing, documentation, macros, environment promotion, and CI/CD integration.
Experience using SQL and Python for data engineering, automation, transformation, and data quality validation.
3 years of experience with pipeline orchestration tools.
Experience with semantic layer development in Snowflake.
Experience working with the data science side of a business.
Ability to communicate in business language, meet with business lines such as finance and marketing, and translate requirements between business stakeholders and engineering.
U.S. citizen or Green Card holder; must be able to work on the employer's W2. Sponsorship is unavailable; C2C and third-party candidates are not accepted.
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.

Nice To Haves

Experience with Snowflake Cortex, AI/ML, and modern analytics engineering practices.
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.
Master's degree in Computer Science, Data Engineering, Information Systems, Data Science, or a related discipline.
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.
dbt certification or equivalent analytics engineering credential.

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 engineering standards and governance 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 and mentorship, including architectural reviews, coaching engineers, evaluating emerging technologies, and supporting advanced analytics, AI/ML, and enterprise data initiatives.