Lead Data Engineer - R01568940

Team: AI & Data Engineering : Data Engineering

Location: Seattle, Washington, United States

Commitment: Employee

Workplace Type: remote

Primary Skills

• AKS, Event Hub, Azure DevOps, Cosmos DB, Azure Functions

Specialization

• Azure Data Engineering Advanced: Senior Data Engineer

Job requirements

• Data Engineer:

  Brillio is looking for a Data Engineer to join the Data Pipeline team. As a member of the team, you will get to live on the front edge of modern technology by building on things like Databricks, Azure Synapse Analytics and Azure Data Explorer while building ETLs and process in Databricks and Data Factory. You will help build state of the art pipelines and data models that are at the heart of the studio decision-making process. You will be working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes. Your customers will be the business, design, test and development teams and they will look to you to help shape how we capture data to improve our test-driven methodologies and build a culture around data driven development.

  The successful candidate will bring an attention to detail along with modern engineering practices and help us build and support a high quality, modern data architecture at scale.

  Qualifications:

  • 5+ years’ experience with SQL required.

  • 5+ years’ experience designing and implementing scalable ETL processes including data movement and quality tools.

  • 3+ years’ experience with modern Big Data Analytics using Data Lake, Spark and formats like Parquet.

  • 2+ years’ experience building cloud hosted data systems. Azure highly preferred.

  Preferred:

  · Building data pipelines in Azure Databricks/Fabric/Spark.

  · Working with data in delta lake format and from Azure Data Explorer/Kusto

  · Applying AI/ML to data engineering use cases (feature engineering, feature stores, model training/serving datasets, and model monitoring data pipelines).

  · Experience preparing and governing datasets for modern AI applications (LLM/RAG, experimentation/A-B testing, and privacy-aware data access).