> Markdown version of [/jobs/ext/1486494-sr-data-engineer](https://www.wearedevelopers.com/jobs/ext/1486494-sr-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Engineer - **Company:** Orion180 Insurance Services, LLC - **Location:** Irving, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Automation of Tests, Microsoft Azure, Big Data, Code Review, Databases, Continuous Integration, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Masking, Data Security, Data Visualization, Relational Databases, DevOps, Digital Assets, Fault Tolerance, Fraud Prevention and Detection, Python (Programming Language), SQL Azure, Raw Data, Power BI, Cloud Services, DataOps, Azure Data Lake, SQL Stored Procedures, SQL Databases, Data Streaming, Transact-SQL, Scripting, Azure Data Factory, Delivery Pipeline, Snowflake, Database Optimization, Apache Spark, Data Layers, Event Driven Architecture, Microsoft Fabric, Data Lakes, Pyspark, Information Technology, Data Lineage, Stream Processing, Azure Synapse Analytics, Stream Analytics, Software Version Control, Data Pipelines, Atlassian Bamboo, Databricks - **Published:** July 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=84fb98656d187561 ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field * United States Citizen or Green Card holder required Experience: * 6+ years of dedicated experience in data engineering, with at least 3 years focused on the Azure ecosystem within a transactional, highly regulated environment. * Core Languages: Mastery of T-SQL and advanced relational database concepts, alongside strong scripting proficiency in Python (or PySpark). * Orchestration & ETL: Extensive hands-on experience building complex control flows, mapping data flows, and parameterizing pipelines in Azure Data Factory. * Big Data Compute: Proven experience processing large-scale data using Spark clusters within Azure Databricks. * Storage & Warehousing: Deep understanding of Azure Data Lake Storage (ADLS Gen2), Delta Lake formatting, and dedicated SQL pools in Azure Synapse. * DevOps: Strong experience using Azure DevOps (Azure Pipelines, Repos) for version control and deploying data engineering workloads. Skills & Attributes: * Excellent analytical, problem-solving, and critical-thinking skills. * Strong communication and collaboration abilities across technical and non-technical teams. * Ability to manage multiple projects in a fast-paced, results-driven environment We are interested in candidates who are developing capabilities aligned with modern, forward-looking analytics practices. This includes experience or familiarity with semantic data modeling and governed metric layers, working within scalable cloud data platforms (e.g., Snowflake, Databricks), and integrating analytics within broader data engineering workflows. Exposure to emerging capabilities such as AI-assisted analysis, automated insight generation, and advanced data visualization techniques is highly valued. ## Description In this role you will be a hands-on technical leader designing, building, and optimizing the data integrations, cloud data infrastructure, and data models that power analytics and data science across the company. You will lead the development of robust data pipelines, orchestrate complex workflows using Azure native tools, and implement data governance frameworks. You will collaborate with data scientists, analysts, and business leaders to turn raw data into scalable, production-ready assets for real-time analytics engines, portfolio risk platforms, and client-facing digital products., * Build and maintain scalable, fault-tolerant ETL/ELT pipelines using Azure Data Factory, Synapse, and Databricks to ingest diverse financial datasets. * Create and optimize data models (dimensional, medallion/bronze-silver-gold) to support PowerBI semantic layers and downstream analytics. * Implement automated data validation, lineage tracking, and monitoring frameworks to ensure the highest standards of data security, privacy, and regulatory compliance. * Develop real-time data streaming and event-driven architecture using Azure Event Hubs and Azure Stream Analytics for instant fraud detection and transaction monitoring. Infrastructure Design & Database Optimization * Engineer and implement highly optimized Lakehouse architectures utilizing Azure Synapse Analytics and Microsoft Fabric, ensuring efficient storage and querying of multi-terabyte data layers. * Design optimized relational, dimensional, and graph data models for diverse analytics computing and workloads. * Manage, optimize, and tune large-scale Azure SQL Databases and Managed Instances, writing complex, highly performant stored procedures and T-SQL queries. DataOps & Automation * Establish robust CI/CD deployment pipelines for all data assets using Azure DevOps, ensuring automated testing, validation, and infrastructure-as-code. * Collaborate with data scientists to productionalize ML feature pipelines and support MLOps workflows. * Implement strict data masking, row-level security, and data lineage workflows using Microsoft Purview to comply with financial regulations. * Act as a technical leader, mentoring junior engineers and promoting modern Agile DataOps engineering practices: CI/CD for data pipelines, testing, version control and code review., While performing general duties for this position, the employee is regularly required to sit, stand, and/or walk around (including the use of stairs). Other demands include the ability to openly communicate with others by talking, listening, comprehending, and reading; being able to lift light objects (<25 lbs); and using standard office equipment such as computers, printers, and phones. In addition, there is an occasional need to bend, twist, or squat down to open/close cabinets and reach for files or other standard office-type objects. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [What Are The Top Skills Required For Azure Developers?](https://www.wearedevelopers.com/magazine/77-what-are-the-top-skills-required-for-azure-developers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)