Data Engineer

Summary

The Data Engineer, Solutions & Data role designs, builds, and operates data pipelines and data integration processes that translate raw data into trusted, usable datasets for analytics, reporting, and downstream solutions. The role focuses on operationalizing pipelines with governance and service expectations (SLAs), improving data quality and reusability, and enabling secure access to integrated data in support of business initiatives. In current initiatives, data engineering includes consolidating data from multiple sources into a central SQL-based integration point and performing field mapping and transformations, so solution teams can consume data consistently.

Location

This position can sit in any of our U.S. offices and offers a hybrid work schedule.

Responsibilities

• Build and operationalize data pipelines across heterogeneous environments, aligning to governance principles and service expectations (SLAs).
• Build and maintain ingestion, transformation, and publication of pipelines (data engineering practice) to deliver analytics-ready data.
• Consolidate data from multiple sources into a centralized integration point (e.g., a single SQL Server instance) and manage field mappings and transformations to support consistent downstream consumption.
• Design and implement data pipelines using Azure data technologies (e.g., Azure Data Factory, Azure Databricks, Azure Event Hubs, SSIS) to ingest, process, and deliver data from sources such as APIs and other systems.
• Build and maintain data warehousing capabilities (e.g., Azure Synapse Analytics) to support analytics and reporting workloads.
• Identify, troubleshoot, and resolve data issues including data quality, integrity, latency, and security concerns; apply monitoring and operational best practices to keep pipelines reliable and performant.
• Contribute to data quality and governance practices, including profiling datasets, defining quality rules, and establishing monitoring/remediation approaches.
• Work cross-functionally with engineers, analysts, and stakeholders to understand requirements and deliver data solutions that support sprint-based delivery.
• Support pod-level delivery by producing reusable data assets and integration components that can be leveraged across multiple initiatives.

Desired Skills

• Proficiency in SQL and Python.
• Data pipeline tooling and cloud data services experience (Azure Data Factory, Azure Databricks, Azure Event Hubs, SSIS).
• Data warehousing experience (Azure Synapse Analytics) and strong fundamentals in data modeling, warehousing, and governance.
• Scripting/automation skills (PowerShell and related tooling) for platform operations and troubleshooting.
• Preferred experience includes familiarity with additional programming languages such as Java, Scala, or Go; experience integrating data from multiple enterprise source systems into a central SQL-based integration layer; and familiarity with DataOps concepts and operating in cross-functional teams that include data engineering personas.

Measures of Success

• Delivering data pipelines with trusted, quality data with agreed service levels.
• Enabling faster onboarding of new data and more consistent analytics/AI consumption.
• Creating reduced manual effort through reusable integrations and standardized transformations.
• Improved data reliability and operational readiness.

Minimum Education

High School or GED

Preferred Education

Bachelor's Degree in Computer Science, Engineering, or related field.

Minimum Years of Experience

3 years of experience in data engineering and/or data platform engineering (pipelines, integration, and operational support).

Pay

• Expected hiring range: $100,787 - $160,255 depending on candidate's geographic market location, experience, skills, educational and professional background, and overall qualifications.
• Comprehensive benefits package including medical/dental/vision insurance and 401(k).

Schedule

This position offers a hybrid work arrangement comprised of remote and in-office work, with the requirement determined in coordination with the hiring manager or supervisor (subject to modification at the firm's discretion).