Senior Data Engineer

Posted 2 Days Ago
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23219, Richmond, VA, USA
In-Office
102K-136K Annually
Senior level
Healthtech • Social Impact • Analytics • Big Data Analytics
The Role
Leads the design, development, and operation of enterprise-scale data platforms and pipelines in Azure. Builds Lakehouse architectures using Databricks, Spark, Azure Data Factory, Synapse, and Data Lake Storage; implements data quality, monitoring, CI/CD, and governance practices; troubleshoots production issues; optimizes performance and scalability; collaborates with analysts, data scientists, and stakeholders; and mentors junior engineers.
Summary Generated by Built In

Position Description

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.

 

Key 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

 

Minimum 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.

 

Critical Skills

  • 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.

 

Additional Skills & Qualifications

  • 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)

 

Education

  • 4-year degree in computer science, engineering or other related IT field of study, or equivalent professional work experience

 

Physical Requirements

  • General office demands
    • Prolonged periods of sitting at a desk and working on a computer.
    • Frequent reaching, handling, and fine manipulation for using office equipment, filing, and managing paperwork.
    • Manual dexterity sufficient to operate a keyboard, mouse, and other office tools.
    • Occasional standing, walking, and bending.
    • Ability to lift up to 10-20 pounds occasionally.
    • Vision abilities required include close vision for computer work and reading documents.
    • Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

 

Skills Required

  • 8+ years of hands-on data engineering experience designing and implementing enterprise-scale data solutions
  • 2+ years of experience developing and operating Azure cloud-native data platforms
  • Expertise in Microsoft SQL Server, Python, pandas, PySpark, Azure Data Factory, Azure Functions, and Azure Data Lake Storage
  • Expertise with Azure Databricks, Spark, PySpark or Scala, Delta Lake, and cluster or job optimization
  • Experience building Data Lakehouse architectures
  • Experience working with CSV, JSON, XML, and Parquet file formats
  • Experience with Git version control and CI/CD pipelines
  • Familiarity with REST APIs for data extraction and integration
  • Proven experience designing and implementing data solutions
  • Understanding of cloud architecture, data warehousing, and modern data stack components
  • Ability to perform root cause analysis on data and processing issues
  • Strong problem-solving and technical communication skills
  • Experience 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 technologies including Azure Event Hubs, Kafka, and Structured Streaming
  • Four-year degree in computer science, engineering, or a related IT field, or equivalent professional experience
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The Company
Year Founded: 1984

What We Do

The United Network for Organ Sharing (UNOS) is a non-profit scientific and educational organization that administers the Organ Procurement and Transplantation Network (OPTN) in the United States. Under contract with the federal government, UNOS manages the national transplant waiting list, matches donors to recipients, and maintains a comprehensive database of transplant data to save and transform lives through research, innovation, and collaboration.

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