Data Engineer

EKN Engineering•Irvine, CA

14d•Onsite

About The Position

EKN Engineering is a fast-growing civil engineering and technology consulting company located in Irvine, CA. We are searching for a Data Engineer to join our rapidly growing organization. We are seeking a talented Data Engineer to join our dynamic team. The ideal candidate will play a pivotal role in leveraging data to drive insights, improve decision-making processes, and enhance the performance of our engineering projects.

Requirements

Bachelor's degree or higher in Computer Science, Engineering, or a related field.
Proven experience in designing, building, and optimizing data pipelines and ETL processes using modern tools and frameworks (e.g. Airflow, dbt, SQLMesh)
Strong programming skills in Python, with experience in developing efficient, maintainable code.
Proficiency in SQL and experience working with relational and NoSQL databases (e.g., PostgreSQL, Azure).
Familiarity with cloud platforms such as AWS or Azure, and hands-on experience with relevant services (e.g., S3, EC2, Redshift).
Experience with data visualization tools (e.g., Power BI, Tableau) and BI concepts is a plus.
Excellent problem-solving skills, analytical thinking, and attention to detail.
Strong communication skills and the ability to collaborate effectively in a team environment.

Nice To Haves

exposure to ML or LLM data workflows, and utility, energy or engineering-sector data.

Responsibilities

Data Modeling: Design and own dimensional and domain-oriented data models that serve as the governed source for reporting, analytics and data science.
Define and document shared business metrics and entities so every dashboard and model operates and is communicated with fidelity.
Partner with analysts, software developers and domain experts to translate business processes into durable schemas that can evolve and scale while maintaining downstream users.
Analytics engineering: Build and maintain a tested, version-controlled transformation layer (dbt, SQLMesh or equivalent), including tests, documentation and lineage.
Apply software engineering practices to analytics code: code reviews, CI/CD, environments and data contracts as both data producer and consumer.
Support semantic layer feeding Power BI, moving analysts to trusted datasets instead of one-off extracts/exports.
AI: Prepare AI-ready data: clean, well-documented, well-governed datasets and metadata that ML models and LLM tools can use reliably.
Build and support pipelines for ML features, retrieval, and unstructured sources like documents and field reports.
Audit AI-generated code, data transformations and technical logic to ensure adherence to compliance standards, performance standards and business requirements.
Design and execute programmatic validation frameworks to test, audit and benchmark LLM outputs.