CodeRoad provides end-to-end software development services, helping businesses scale with ideal infrastructure solutions. From staff augmentation to dedicated IT teams and general software engineering, our nearshore technology services empower businesses to thrive in an ever-evolving digital landscape.
About the Role
As a Senior Data Engineer, you will serve as the technical backbone of our data architecture, owning end-to-end data pipelines and driving org-wide decisions that elevate how complex data ecosystems are built, normalized, and maintained. You will hands-on design and scale robust architectures using modern cloud warehouses like BigQuery, leverage dbt or Dataform for transformations, and construct reliable production services in Python to keep critical business data flowing seamlessly.
This role is critical to transforming heterogeneous source systems into a single source of truth, directly powering high-impact decisions across Finance and Operations. You won't just deliver pipelines, you will anchor the architectural standards, testing frameworks, and monitoring protocols that guarantee data trustworthiness, resilience, and operational excellence across the enterprise.
Key Responsibilities
Design and implement standardized, normalized data models capable of integrating heterogeneous and complex source systems.
Lead org-wide architectural decisions for data warehousing, orchestration, and pipeline reliability across the data engineering ecosystem.
Build and maintain production-grade data services and internal tooling utilizing Python and modern frameworks like FastAPI or Flask.
Optimize automated data transformations and workflow orchestration using tools like dbt, Dataform, and Apache Airflow or Google Cloud Composer.
Establish comprehensive data quality frameworks, including automated testing, anomaly detection, monitoring, and structured incident response processes.
Collaborate with cross-functional partners in Finance and Operations, translating complex technical tradeoffs into clear business strategies.
Requirements
5+ years of hands-on data engineering experience, including 2–3+ years operating at a staff or principal level with org-wide architectural scope.
Tech Stack Mastery: Deep expertise in BigQuery (or equivalent cloud data warehouse) and SQL-based transformation tools like dbt or Dataform.
Advanced Python Proficiency: Proven track record building and maintaining production services, microservices, or APIs (FastAPI, Flask, or equivalent).
Production Orchestration: Hands-on experience managing complex workflows at scale using Airflow, Cloud Composer, or similar orchestrators.
Data Modeling & Trustworthiness: Demonstrated success designing normalized models from complex sources, paired with robust testing, monitoring, and incident response practices.
