Software Engineer II Data

CDW•Virtual - Illinois, IL

5d•$86,000 - $119,000

About The Position

The Software Engineer II, Data builds and supports enterprise data ingestion, transformation, modeling, and integration solutions. The role translates business and source-system requirements into scalable, maintainable data solutions for analytics, reporting, operational, and downstream application needs. The position develops ingestion and transformation solutions with Microsoft Fabric, Azure Data Factory, Azure Data Lake/Storage, and Azure SQL/SQL Server. It implements batch, incremental, API, file, database, and event-based integration patterns; develops curated and dimensional data models; writes and tunes complex SQL and data-processing logic; and addresses data-quality, integrity, completeness, and consistency issues. The engineer uses GitHub, Azure DevOps, and established CI/CD, pull-request, code-review, and design-review practices. The role also monitors and supports production pipelines and scheduled workloads, investigates failures and data-quality issues, and contributes reusable engineering patterns and documentation. CDW describes a collaborative environment that values curiosity, continuous learning, responsible adoption of AI, and an equitable, transparent hiring process. Pay is listed as $86,000–$119,000, depending on experience and skill set. Benefits information is provided at https://cdw.benefit-info.com/.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Engineering, or a related field and 3 years of relevant IT experience, or 7 total years of relevant IT experience.
Professional experience developing enterprise data pipelines, integrations, data warehouses, Lakehouse solutions, or analytics data platforms.
Demonstrated ability to troubleshoot complex data and integration problems.
Strong analytical and problem-solving skills.
Strong communication and cross-functional collaboration skills.
Ability to deliver effectively in an Agile enterprise engineering environment.
Strong SQL and relational database development.
Strong understanding of data engineering and ETL/ELT.
Experience or skills with Microsoft Fabric.
Experience or skills with Azure Data Factory (ADF).
Experience or skills with Azure Data Lake / Storage.
Experience or skills with Azure SQL / SQL Server.
Knowledge of data warehousing and dimensional/data modeling concepts.
Knowledge of data ingestion and transformation patterns.
Knowledge of data validation and reconciliation.
Knowledge of performance optimization and troubleshooting.
Git / GitHub.
CI/CD and pull-request-based development.
Azure DevOps / Agile delivery practices.

Nice To Haves

Python and/or PySpark.
Delta Lake / Lakehouse concepts.
Medallion architecture.
REST APIs / JSON-based integrations.
Event-driven integrations and Azure Service Bus.
Data migration and legacy-platform modernization.
Azure Key Vault and secure credential handling.
Enterprise data governance and data-quality concepts.
Microsoft Fabric / Azure data engineering certifications.

Responsibilities

Build ingestion and transformation solutions using Microsoft Fabric and Azure Data Factory.
Implement pipelines for batch, incremental, API, file, database, and event-based integration patterns.
Develop reusable and configuration-driven ingestion and transformation patterns where appropriate.
Build curated data structures for analytics, reporting, operational, and downstream application requirements.
Work with structured and semi-structured data, including SQL, CSV, JSON, XML, and Parquet.
Translate business and source-system requirements into scalable data models.
Participate in conceptual, logical, and physical data modeling.
Design dimensional and curated models for enterprise reporting and analytics.
Develop complex SQL queries, views, transformations, and data-processing logic.
Troubleshoot query, pipeline, and data-processing issues; tune query and pipeline performance.
Identify and resolve data-quality, integrity, completeness, and consistency issues.
Perform source-to-target and source-to-source reconciliation.
Develop validation checks for new pipelines and changes.
Use GitHub for source control and branch-based development.
Follow EDAS CI/CD, pull-request, code-review, and design-review standards.
Use Azure DevOps and approved engineering practices to manage delivery.
Create maintainable, reusable, well-documented code.
Participate in peer code and design reviews.
Create or improve reusable engineering patterns and supporting documentation.
Monitor and support production data pipelines and scheduled workloads.
Troubleshoot pipeline failures, missed schedules, long-running processes, and data-quality issues.