Senior Data Engineer: Azure Lakehouse & Real-Time Analytics

Orion180 Insurance Services LLC; Irving (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

On-site role

Competitive pay

Performance bonuses

Medical, dental, vision

401k

Paid holidays & PTO

Job summary

Orion180 seeks a Sr. Data Engineer to design, build, and optimize data integrations, cloud data infrastructure, and data models powering analytics and data science across the company. You will lead robust ETL/ELT pipelines and real-time analytics initiatives using Azure technologies and Databricks.

Collaborate with data scientists and business leaders to productionalize ML feature pipelines, implement data masking and governance, and mentor junior engineers in a fast-paced, on-site environment.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related quantitative field
• 6+ years of data engineering experience with 3+ years in Azure-focused environments
• Strong T-SQL, Python (PySpark) and relational DB concepts
• Experience with data pipelines, data warehousing, and governance frameworks

Responsibilities

• Design, build, and optimize data integrations and data models for analytics and data science.
• Lead development of robust ETL/ELT pipelines using Azure tools and Databricks.
• Collaborate with data scientists and analysts to productionalize ML feature pipelines and support MLOps.
• Establish CI/CD pipelines for data assets and enforce data security and governance.

Skills

T-SQL

Python

Azure Data Factory

Azure Synapse

Databricks

PowerBI

Education

Bachelor's or Master's in CS/Engineering/Math

Tools

Azure Event Hubs

Azure Stream Analytics

Azure DevOps

Microsoft Purview

Job description

Orion180 seeks a Sr. Data Engineer to design, build, and optimize data integrations, cloud data infrastructure, and data models powering analytics and data science across the company. You will lead robust ETL/ELT pipelines and real-time analytics initiatives using Azure technologies and Databricks.

Collaborate with data scientists and business leaders to productionalize ML feature pipelines, implement data masking and governance, and mentor junior engineers in a fast-paced, on-site environment.