Data Engineer

About the role

Engagement Type: [Full-Time / Contractor]

About CodeRoad

CodeRoad is a technology execution partner trusted by companies across North America since 2002. We work alongside our clients to engineer, modernize, and scale the technology that powers their businesses, combining deep technical expertise, specialized teams, and shared accountability for outcomes. Our capabilities span product engineering, platform engineering, legacy modernization, and AI engineering.

Our engineers, designers, delivery leaders, and technology specialists collaborate through Velocity Studios: specialized teams developed within CodeRoad to deliver complex technology initiatives. Being assigned to a project means you become part of a collaborative environment where you can strengthen your expertise, take ownership of meaningful work, and grow alongside experienced professionals.

Why Build Your Career at CodeRoad?

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High-Velocity Work & Client Exposure: We work close to the technology, teams and roadmaps that move real outcomes. Direct engagement with U.S. client teams and roadmaps.

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Career Continuity & Long-Term Opportunity: Your career here can outlast any single project. Continuous multi-project pipeline; long-tenure CodeRoaders.

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Engineering Autonomy & Technical Mastery: We invest in the skills that shape what's next. Agentic AI tools and training, Coursera Enterprise, Studio practice labs, L1-L7 Career Framework.

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High-Trust Employee Experience: You grow in an environment built on trust, transparency and recognition. High-touch onboarding, transparent leadership communication, community recognition, psychological safety.

Key Responsibilities

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Build & Maintain Scalable Data Pipelines: Develop, optimize, and maintain reliable ETL/ELT pipelines to ingest, transform, validate, and load transactional data into relational databases, particularly Microsoft SQL Server and PostgreSQL.

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Support AI & Data-Driven Solutions: Contribute to data ingestion and preparation workflows that support AI applications, including document processing, metadata management, chunking, and embedding generation for Retrieval-Augmented Generation (RAG) systems.

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Leverage AI-Assisted Engineering: Proactively use modern AI tools to accelerate code development, improve data transformation workflows, troubleshoot issues, and identify opportunities to make data engineering processes more efficient.

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Orchestrate & Monitor Data Workflows: Develop, schedule, and monitor data processing jobs using tools such as Airflow, Azure Data Factory (ADF), and SQL Server Agent. Investigate pipeline failures and implement data quality checks to ensure reliable processing.

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Improve Database & Data Model Performance: Write and optimize SQL queries, contribute to data modeling, and improve the efficiency and reliability of database operations and analytical data structures.