Senior Data Engineer

UNITED NETWORK FOR ORGAN SHARING•Richmond, VA

12d•$101,973 - $135,963•Onsite

About The Position

We are seeking a Senior Data Engineer to lead the design and implementation of end-to-end data solutions within a modern Azure-based cloud environment. This hands-on, technical role will partner closely with data scientists, analysts, and business stakeholders to ensure that data is accurate, accessible, and optimized for analytics, reporting, and operational needs. You will play a key role in building Data Lakehouse, scalable data pipelines and integrations while championing best practices, mentoring junior engineers, and leading strategic data initiatives across teams.

Requirements

8+ years of hands-on experience in data engineering, including designing and implementing enterprise-scale data solutions.
2+ years of experience developing and operating Azure cloud-native data platforms.
Expertise in MS SQL Server, Python (pandas, PySpark), Azure Data Factory, Azure Functions and Azure Data Lake Storage.
Strong expertise with Azure Databricks, including Spark (PySpark/Scala), Delta Lake, and cluster/job optimization.
Solid understanding and hands-on experience building Data Lakehouse architecture
Experience working with a variety of file formats (e.g., CSV, JSON, XML, Parquet).
Experience with version control (Git) and CI/CD pipelines for data engineering workflows
Familiarity using REST APIs for data extraction and integration.
Proven experience designing and implementing data solutions.
Strong understanding of cloud architecture, data warehousing and modern data stack components.

Nice To Haves

Demonstrated ability to perform root cause analysis on data and processing issues
Strong problem-solving skills with the ability to explain technical concepts to non-technical audiences
A successful history of manipulating, processing and extracting value from large disparate datasets
Experience with Big Data technologies such as Databricks, Spark, or Azure Synapse
Knowledge of CI/CD workflows, version control, and agile development practices
Familiarity with data governance, privacy, and compliance frameworks
Experience with data warehousing, analytics tools, and BI platforms
Familiarity with streaming data technologies (Azure Event Hubs, Kafka, Structured Streaming)

Responsibilities

Architect and implement secure, scalable data pipelines using Azure Data Factory, Azure Functions, and Azure Data Lake Storage
Design, develop, and maintain scalable ETL/ELT pipelines using Azure Databricks (PySpark/Spark SQL) to ingest, transform, and process large volumes of structured and unstructured data
Build and manage data pipelines using Azure services such as Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage (ADLS Gen2), Azure Event Hubs, and Azure SQL Database
Implement data quality, validation, and monitoring frameworks to ensure reliability and accuracy of data pipelines
Collaborate cross-functionally with data scientists, analysts, and business stakeholders to understand data requirements and deliver fit-for-purpose data solutions
Lead modernization and optimization efforts, improving pipeline performance, maintainability, and scalability
Support CI/CD practices for data pipelines using tools such as Azure DevOps, Git, and Databricks Repos
Detect and resolve data quality issues; implement automated audits and monitoring processes
Troubleshoot and resolve production data pipeline issues, ensuring high availability and minimal downtime
Act as a technical leader on schema design, performance tuning, and Azure data architecture
Mentor and support junior engineers across data engineering, analytics, and BI teams
Participate in and lead code reviews, promoting clean, well-documented, and testable code
Stay current on trends in data engineering and cloud technologies, identifying opportunities to innovate