Strong SQL/T-SQL skills, including complex joins and query performance tuning.
Experience building reliable data pipelines with incremental loads and alert systems.
Proficient in dimensional modeling for effective Power BI semantic model design.
Ability to communicate data concepts clearly to non-technical stakeholders.
Self-directed with a capacity to prioritize tasks independently.
Responsibilities
Establish the Azure data platform by migrating existing data pipelines and validating data integrity.
Design and maintain Azure Data Factory pipelines for diverse data sources, ensuring robust error handling and alerts.
Manage the lakehouse architecture and oversee the contents of staging Azure SQL databases.
Identify and implement improvements for platform reliability and cost-effectiveness post-migration.
Develop and maintain a shared Power BI semantic model for cross-departmental reporting.
Act as the data subject matter expert within the IT team, ensuring documentation and version control of data pipelines.
Perform Level 1 IT tasks on-site as needed, including hardware setup and troubleshooting.
Benefits
Remote-first work environment with occasional on-site requirements.
Opportunity to work with cutting-edge Azure technologies.
Collaboration with cross-functional teams including Finance and Revenue.
Potential for professional growth in data engineering and analytics.
Full Job Description
About the role
The Data Engineer builds and runs the Azure data platform that turns ticketing, point-of-sale, and other business data into reporting our teams can trust. In the first phase, this role stands up our existing Azure Synapse, Azure Data Lake Storage, and Azure Data Factory platform in a new Azure environment; after that, it leads the work of improving it, including evaluating Microsoft Fabric. The role is the data subject matter expert on the IT team and partners closely with Finance, the Revenue team, and IT leaders. It is remote-first and based in the Chicago area, with on-site time as needed for hands-on Level 1 IT tasks (up to about four hours per week) and occasional meetings
Compensation:
$80,000-90,000USD/annually
Extended Benefits
What you'll do
Stand up our data platform in a new Azure environment: rebuild and migrate the existing Azure Data Factory pipelines, Synapse workspace, Azure Data Lake Storage (ADLS Gen2), and staging Azure SQL databases from our current template, and validate that the migrated data matches the source
Design, build, and maintain ADF pipelines that ingest data from on-premises SQL Servers, cloud native ticketing systems, and other sources, including incremental loads, parameterization, triggers, error handling, and alerting on pipeline failures and data freshness
Own the lakehouse architecture (bronze, silver, and gold layers) and the contents of the staging Azure SQL databases: schemas, stored procedures, load and transformation logic, and query performance (backups, patching, and access are handled by the System Admin team)
After the migration, find and deliver improvements to the platform's reliability, cost, and design, and help evaluate whether Microsoft Fabric is the right next step
Build and maintain a shared Power BI semantic model that Finance, Revenue, and IT leaders report from, with consistent definitions for metrics such as attendance, admissions revenue, and per capita spending; build key reports and help those teams build their own
Serve as the data SME within the IT team: define data standards, keep pipelines and data definitions documented well enough for someone else to pick up, and keep pipeline code in version control
As the only IT team member on site at this location, handle Level 1 tasks that need to be done in person (hardware setup and swaps, printer and peripheral issues, hands-on troubleshooting), up to about 4 hours per week; remote support is handled by the team in Vancouver
Builds reliable pipelines: incremental (watermark) loads, parameterized and reusable pipeline design, retries, and alerts that reach a person when a load fails or quietly loads nothing
Understands dimensional modeling (star schemas, facts and dimensions, grain) well enough to design data that a shared Power BI semantic model can be built on
Able to explain data and trade-offs in plain language to Finance, Revenue, and IT leaders, and to turn their questions into well-structured data
Self-directed: comfortable owning a domain and setting priorities with minimal oversight while collaborating with the System Admin team and the wider IT team
Education & Experience
EDUCATION:
Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience
IDEAL EXPERIENCE: You won't need every item on this list. If you meet most of it, we encourage you to apply.
3+ years in data engineering, including at least 2 years building and running production Azure Data Factory pipelines
Hands-on Azure Synapse Analytics and Azure Data Lake Storage (ADLS Gen2), plus Azure SQL Database
Designing layered (bronze, silver, gold) lakehouse or warehouse architectures, on any platform
Moving data from on-premises SQL Server into Azure, including use of the self-hosted integration runtime
Preferred: Microsoft Fabric (lakehouse, warehouse, or pipelines); building a shared Power BI semantic model with DAX measures; Power BI report development and service administration (workspaces, refreshes, row-level security); Python/PySpark; Git and Azure DevOps; migrating a data platform between Azure environments; transactional data from ticketing or POS systems and attraction metrics (attendance, admissions revenue, per capita spending, booking pace); small-team background wearing multiple hats
