Senior Data Engineer

Further•Cleveland, OH

7h•Hybrid

About The Position

We are looking for a highly skilled and strategic Senior Data Engineer to lead in our data engineering consulting team. In this role, you will serve as the technical cornerstone for our clients, designing and deploying the sophisticated data architectures required to support production-grade Artificial Intelligence and Machine Learning applications. This is a high-impact role where you will set the standards for data quality, modeling, and orchestration across the organization. You will navigate diverse technical environments, and your work will directly enable the transition from experimental AI prototypes to resilient, enterprise-scale systems that deliver measurable business value.

Requirements

6+ years of data engineering experience with a focus on modern cloud platforms.
Expert-level proficiency in Python and SQL is mandatory.
Strong software engineering background with proficiency in Python for building custom integrations and data tools.
Deep production experience with dbt for data modeling and Airflow for orchestration.
Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure.
Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant.
Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture.
Experience with AI workflows and data systems like vector databases, PostgreSQL, Vertex AI, Pub/Sub.

Nice To Haves

Experience architecting feature stores or data pipelines specifically for ML workloads.
The ability to navigate diverse technical environments and corporate cultures while maintaining a high standard of delivery and client satisfaction.
A commitment to software engineering best practices, including version control, unit testing, and comprehensive documentation.
An analytical thinker who anticipates scaling bottlenecks and security vulnerabilities before they impact production.

Responsibilities

Architect and build scalable, high-volume ELT/ETL pipelines that ingest complex data sets from diverse client systems, including legacy on-premise databases, ERPs, and third-party APIs, into centralized cloud warehouses.
Lead the audit and optimization of existing data workflows, refactoring inefficient queries and data models to significantly reduce latency and operational costs for enterprise-scale environments.
Design and implement comprehensive data quality frameworks, ensuring automated testing and validation logic catches anomalies before they reach production models or executive dashboards.
Design and implement data pipelines specifically for AI workloads, including the management of vector databases to support Retrieval-Augmented Generation (RAG) and model inference.
Own the roadmap for data platform stability and scalability enhancements, ensuring the infrastructure can support the evolving needs of both corporate IT and AI research teams.
Provide technical mentorship to the engineering team, conducting rigorous code reviews and driving improvements in coding standards, documentation, and system reliability.
Standardize the organization’s approach to the Modern Data Stack (MDS), driving the adoption of best practices in dbt, Airflow (Cloud Composer), and Beam (Dataflow) across diverse client engagements.