Data Engineer - AI, Hybrid

The application window is expected to close on: 08/28/2026

Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.

This position is based in North Carolina and operates under a hybrid work model.

We are a Growth Metrics high-performing Data and Analytics team on a mission to embed AI into every layer of our decision-making. By demonstrating machine learning, large language models (LLMs), and automation, we're redefining how data powers innovation and efficiency  team is a specialized in driving data architecture and strategic business outcomes. We serve as the backbone for critical initiatives across CX, Finance, and GTM, ensuring our data infrastructure is both scalable and high performing.

Our culture is collaborative and fast-paced, where we prioritize technical excellence and continuous growth. We are particularly excited about integrating cutting-edge AI and machine learning capabilities into our data workflows to solve complex business challenges.

As a Senior Data Engineer, you will design, build, and oversees the deployment and operation of technology architecture, solutions, and software to capture, manage, store, and utilize structured and unstructured data from internal and external sources. Establishes and builds processes and structures based on business and technical requirements to channel data from multiple inputs, route appropriately, and store using any combination of distributed (cloud) structures, local databases, and other applicable storage forms as required.

Develops technical tools and programming that leverage artificial intelligence, machine learning, and big-data techniques to cleanse, organize, and transform data and to maintain, defend, and update data structures and integrity on an automated basis. Creates and establishes design standards and assurance processes for software, systems, and applications development to ensure compatibility and operability of data connections, flows, and storage requirements.

Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs.

• Data Engineering & Architecture:
  Design and implement scalable data architectures to store, process, and analyze high-volume datasets. Manage the development of robust data pipelines, improve feature engineering, and apply deep knowledge of databases, cloud services, and scripting languages to build innovative models.

• Quality, Security, & Compliance:
  Maintain high data quality through rigorous validation, scrubbing, and imputation techniques. Write clean, functional code with unit tests, conduct thorough code reviews to identify risks, contribute to feature threat modeling, and ensure adherence to data compliance and security standards.

• Collaboration & Business Impact:
  Partner with data scientists and business stakeholders to articulate and resolve complex problems. Design reporting and visualization tools that communicate actionable insights and link data outcomes directly to business goals.

• Project Leadership & Strategy:
  Lead moderately complex, end-to-end projects and drive technical analysis from initial design through completion. Recommend optimal data sources, methodologies, and analytics tools to address evolving business needs.

• Bachelor's in Computer Science, Data Engineering, AI/ML, Data Science, or equivalent 8+ years of related development experience, or Master's with 5+ years.

• 4+ years hands on Data engineering experience on Snowflake, Snowflake Intelligence/Co-work, DBT, Query optimization and performance, Data modeling and schema design, Incremental processing and pipelines, Data quality and validation, Optimize database query performance, Warehouse architecture and scaling, Cost optimization strategies, ETL/ELT tooling and orchestration.

• Design and implement complex AI agent orchestration systems using Lang Graph and Lang Chain for enterprise-scale automation.

• Proficiency in programming languages commonly used in data engineering (e.g., SQL(Snowflake), Teradata, Sophisticated Python, Java, Scala, etc.).

• Demonstrated expertise in designing and maintaining complex data architectures and pipelines.

• Proven experience in data engineering, specifically with cloud-based storage and distributed structures.

• Experience managing data validation, scrubbing, and correlation methodologies for large-scale datasets.

• Experience applying AI and machine learning techniques to data engineering problems.

• Hands-on experience in developing and deploying AI/ML solutions in enterprise production environments.

• Experience with large language models (LLMs), transformer architectures, and advanced AI frameworks

• Advanced degree in AI/ML, Computer Science, or related technical field

• Deep knowledge of vector databases, retrieval-augmented generation (RAG), and semantic search…