This role pays less than
68%
of similar roles. Most pay
$140,262–$211,200
— the shaded band above.
At the midpoint, this role pays about
$152k
versus about
$176k
for comparable roles.
Based on 240 similar postings.
Employer
About CoStar Group
CoStar Group is the leading provider of commercial real estate information, analytics, and online marketplaces, including CoStar, Apartments.com, and LoopNet platforms. Industry: Commercial Real Estate Data & Analytics
CoStar Group currently has
65 open roles
on FindRole.
Listed pay typically runs
$133,000–$190,000
across 63 roles with salary data.
The Senior Data Engineer will join the finance tech organization to build next-generation data warehousing pipelines and ecosystems. This role focuses on developing migration and transformation processes for external systems used in an enterprise contracting system, involving the ingestion of diverse data from multiple sources into a repeatable, automated state. The candidate will design and maintain scalable platforms, optimize ETL/ELT processes, and standardize billing data to improve visibility into large-scale financial transactions. Required skills include advanced SQL, Python, Scala, Java, or C#/.NET, along with experience in distributed processing engines like Databricks or Spark, cloud warehouses such as Snowflake or BigQuery, and orchestration tools like Airflow. The role involves managing heterogeneous sources including REST/GraphQL APIs and SFTP feeds while implementing data quality controls within AWS, Azure, or GCP environments to solve complex financial data integration challenges.
What does a Data Engineer earn in Virginia?
Median $211200 from 85 postings across 6 companies.
Design, build, and maintain scalable data pipelines and platforms for the finance tech organization.
Develop and optimize ETL/ELT processes to handle large, complex datasets from various sources.
Build automated systems to process diverse data formats into a repeatable and customizable state.
Standardize data environments across billing systems to improve visibility into patterns and errors.
Normalize inconsistent third-party schemas into standardized enterprise data models including deduplication and entity resolution.
Implement data quality, validation, and reconciliation controls for externally sourced data.
Create applications to automate the conversion of financial data across internal systems and acquisitions.
Mentor other engineers and contribute to technical design decisions for production systems.
What we're looking for
Bachelor's degree from an accredited, not-for-profit, in-person college or university.
5+ years of data engineering experience including setting technical direction and influencing design across a team.
Experience building batch and incremental ingestion pipelines from heterogeneous sources like REST/GraphQL APIs, flat files, SFTP, and database replication.
Experience normalizing inconsistent third-party schemas into standardized enterprise data models with source-to-target mapping and entity resolution.
Advanced SQL and data modeling skills combined with proficiency in Python, Scala, Java, or C#/.NET.
Production experience with distributed processing engines, cloud data warehouses/lakehouses, and pipeline orchestration tools.
Experience implementing data quality controls, version control, and CI/CD in a major cloud environment (AWS, Azure, or GCP).
Experience with agentic engineering using AI-assisted development tools to accelerate software delivery.
Production-scale expertise with Databricks or equivalent lakehouse platforms and open table formats (preferred).
Experience with modular transformation frameworks like dbt or SQLMesh (preferred).
Experience with managed ingestion/replication tooling and streaming technologies such as Kafka or Flink (preferred).
Experience with data quality, observability, cataloging, and governance tools (preferred).
Experience with master data management, entity resolution, and geospatial standardization (preferred).
Experience with AWS services, Terraform, and backend data services in C#/.NET (preferred).
Ability to apply AI/ML feature engineering and agentic workflows for code generation and root-cause analysis (preferred).
Azure
Python
SQL
Spark
Databricks
Azure Data Factory
Data Lake
Synapse Analytics
Azure Functions
ETL
Hadoop
NoSQL
Big Data
Data Modeling
Data Warehousing
DevOps
Real-Time Processing