Data & AI Platform Engineer

. Design, create and maintain data pipelines to collect, clean, transform, and load data from various sources.

Posted 4/21/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $117,000 - $191,000 per yearWebsite

Tools & technologies

CloudCyber SecurityDockerETLPythonSQL

Key responsibilities & impact

Design, create and maintain data pipelines to collect, clean, transform, and load data from various sources.
Collaborate with interdisciplinary teams of environmental engineers, data scientists, and software developers to understand data requirements.
Participate in the design of and execute the creation and management of data warehouses, data lakes, and databases.
Develop, deploy, execute, and monitor ETL (Extract, Transform, Load) processes.
Develop and maintain data models and engage in SQL database management and querying.
Design and execute testing plans for data pipeline and data warehousing implementation efforts.
Implement processes for improving data quality and managing data governance.
Collaborate with IT infrastructure and cybersecurity teams to implement and operate data pipelines within approved data infrastructure.
Design and execute processing tasks using Python and maintain up-to-date understanding of big data processing frameworks.
Perform regular data audits and updates to ensure a high level of data accuracy and integrity.

What you’ll need

Typically a minimum of 5 years of data engineering or related experience.
Typically certified in BC's SMS Framework and progressing through the SMS competencies.
Understanding in building and optimizing data pipelines, architectures, and data sets.
Strong working SQL knowledge and skills in implementing and managing relational databases.
Proficient in ETL processes creation and management and techniques for data cleaning and validation.
Proficient in Python and other scripting languages applicable for data engineering.
Proficient with best practices for writing clean, maintainable, and scalable code while applying software engineering best practices including use of version control systems (e.g., Git).
Demonstrated abilities with data warehousing solutions, data lake solutions, and cloud platforms.
Hands-on experience supporting production LLM‑ or RAG‑based systems in a platform, data, or MLOps capacity is preferred.
Familiarity with LLMOps practices and operational tooling is preferred.
Exposure to analytics platforms and integration‑heavy systems is preferred.
Experience deploying and operating AI‑enabled or analytics-heavy services in Docker‑based containerized runtimes on managed cloud platforms is preferred.
Familiarity with geospatial data and analysis is preferred.
Interest or experience in environmental, water resources, or scientific computing domains is preferred.

Comp & perks

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Hard Skills & Tools

data pipelinesETLSQLdata modelingPythondata warehousingdata lakesdata governancedata cleaningdata validation

Soft Skills

collaborationcommunicationproblem-solvingattention to detailinterdisciplinary teamwork

Certifications

BC's SMS Framework