Lead Data Engineer

🕒 July 24

☕ Washington – Remote

☕ Washington – Remote

💵 $120k - $125k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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Brillio

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🏥 Healthcare

🛡️ Insurance

📦 Logistics

💰 Private Equity Round on 2019-01

Healthcare • Insurance • Logistics

Brillio is a technology consulting company that specializes in driving digital transformation for businesses across various industries such as banking, financial services, insurance, life sciences, telecom and media, healthcare, and retail. They focus on customer experience transformation, data and AI, product and platform engineering, and provide infrastructure, cloud, and security solutions. With an emphasis on engineering digital products and leveraging generative AI, Brillio aims to provide bespoke solutions that help businesses thrive in the age of AI and technological disruption. They also offer services to help businesses adapt to new technological challenges and turn disruptions into competitive advantages.

📋 Description

• Building state of the art pipelines and data models that are at the heart of the studio decision-making process. • Working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes.

🎯 Requirements

• 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).