Data Engineer, AI & Business Intelligence- Park City UT

Position Summary

Event Network is seeking a mid-level Data Engineer to develop and support the pipelines, integrations, curated datasets, models, and services behind our artificial intelligence and business intelligence initiatives.

This is a hands-on engineering role for someone who can turn defined business and analytical needs into secure, supportable production solutions.

Event Network is hiring for one Data Engineer position.

The position may be based at either our San Diego, California headquarters or our Park City, Utah headquarters. Hiring a candidate at either location will fulfill this single opening.

You will partner closely with the Senior Business Analyst leading our AI and BI efforts and with other Information Systems resources. A central part of the role is strengthening our software delivery discipline through Azure DevOps, Git-based version control, CI/CD, automated testing, and controlled deployment practices for data solutions.

This is a hybrid position with a normal schedule of four days onsite at the employee's assigned Event Network headquarters location and one day working remotely each week.

Applicants must be legally authorized to work in the United States. Event Network is unable to provide employment-based immigration sponsorship for this position now or in the future.

What You Will Do

• Design, build, maintain, and support scheduled, incremental, and event-driven data pipelines from databases, APIs, operational applications, files, and approved Microsoft 365 sources.
• Develop transformations and curated data products for Azure Databricks, Unity Catalog, Power BI semantic models, analytics, and approved AI applications.
• Own code and deployment hygiene in Azure DevOps: organize repositories, use branches and pull requests appropriately, maintain useful commit history, apply peer-review practices, and keep production code out of personal or unmanaged locations.
• Establish and maintain CI/CD pipelines that validate, package, and promote data pipelines, notebooks, SQL, infrastructure, and configuration across development, test, and production environments.
• Build automated tests for transformation logic, pipeline behavior, schemas, data quality, reconciliations, and regression scenarios; integrate those tests into pull-request and deployment workflows.
• Implement configuration management, environment-specific settings, secret handling, approvals, rollback or recovery procedures, and auditable releases.
• Create reliable ingestion and retrieval processes for approved documents and structured data used by AI solutions, including metadata, indexing, security-aware access, evaluation data, and telemetry where appropriate.
• Implement monitoring, logging, alerts, retry and reprocessing patterns, and production support procedures for assigned pipelines, datasets, integrations, indexes, and services.
• Profile and reconcile data, detect missing or duplicate records, validate business rules and control totals, and prevent incomplete loads from being presented as successful.
• Maintain technical documentation, source-to-target mappings, deployment instructions, runbooks, and change histories as part of the definition of done.
• Communicate progress, tradeoffs, risks, data limitations, and blockers early and clearly to technical and business partners.

Required Qualifications

• Approximately 3-5 years of relevant experience in data engineering, analytics engineering, database development, or integration development; equivalent practical experience will be considered.
• Strong SQL skills, including complex queries, joins, aggregations, window functions, common table expressions, and performance troubleshooting on SQL Server or a comparable relational platform.
• Working proficiency in Python and experience building reusable, readable code for structured data, APIs, and common file formats.
• Experience building and supporting production ETL/ELT pipelines, including incremental processing, orchestration, logging, error handling, monitoring, and recovery.
• Hands-on Git experience and a clear understanding of branches, pull requests, code review, merge practices, release history, and resolving conflicts.
• Experience creating or maintaining CI/CD pipelines in Azure DevOps or a comparable platform, including automated validation and controlled promotion between environments.
• Experience designing automated unit, integration, data-quality, schema, or regression tests and incorporating them into engineering workflows.
• Understanding of development, test, and production separation; secure configuration and secrets management; and repeatable deployment practices.
• Experience preparing governed, reusable data for Power BI or a comparable BI platform and working with facts, dimensions, relationships, and semantic models.
• Ability to investigate ambiguous data problems, ask focused questions, and collaborate effectively with both business and technical stakeholders.

Preferred Experience

• Azure Data Factory, Azure Databricks, Unity Catalog, Azure Data Lake Storage, Microsoft Fabric or OneLake, and Power BI semantic models.
• Azure DevOps Repos and Pipelines, infrastructure as code such as Bicep or Terraform, Databricks Asset Bundles or comparable deployment automation, and test frameworks such as pytest.
• Azure Key Vault, Microsoft Entra ID, Azure Monitor, Application Insights, Log Analytics, or Microsoft Purview.
• Microsoft Dynamics NAV or Business Central, LS Retail, retail sales and inventory data, e-commerce, SFTP, partner-file integrations, and financial or operational reconciliation.

Section 2 | Detailed Role & Working Arrangement

This section provides internal detail on how the role will operate, how responsibilities are divided, and what production-ready delivery means in Event Network's environment. It can be used during interviewing, onboarding, goal setting, and performance discussions.

Role Purpose and Boundaries

The Data Engineer is the primary engineering implementation partner for the Senior Business Analyst responsible for AI and business intelligence. The role provides the technical capacity to investigate sources, build reusable data products, productionize approved concepts, and support them after release.

This is not primarily a Power BI report developer, business analyst, data scientist, machine-learning researcher, prompt engineer, database administrator, general IT support role, enterprise architect, or management position. The engineer may contribute in those areas, but the core accountability is production data engineering.

Working Partnership

The Senior Business Analyst will generally lead:

• Business opportunities, use cases, priorities, intended users, and expected outcomes.
• Stakeholder engagement, analytical requirements, business definitions, and initial acceptance criteria.
• AI experimentation, solution concepts, evaluation of business-facing results, and confirmation that the delivered solution addresses the intended need.

The Data Engineer will generally lead:

• Technical discovery, data-source investigation, feasibility, implementation approach, effort estimates, and identification of security, quality, dependency, or operational risks.
• Pipeline, transformation, data-model, retrieval, integration, testing, deployment, monitoring, recovery, and technical documentation work.
• Routine technical decisions about code organization, query construction, implementation patterns, validation, logging, troubleshooting, and performance improvements that do not change approved business meaning.

The two roles will collaborate on data availability, business meaning, security, quality, technical risk, expected value, and readiness for business adoption. Significant decisions involving enterprise architecture, platform selection, sensitive data, material cost, or cross-system impact will follow the appropriate Information Systems review process.

Engineering Standards and Delivery Controls

Definition of done: A solution is not complete merely because it runs once. It must be versioned, reviewed, tested, deployable, observable, documented, secure, and recoverable in proportion to its production risk.

Azure DevOps repos and Git: Place production code, notebooks, SQL, pipeline definitions, infrastructure definitions, deployment templates, and relevant configuration in approved repositories. Use meaningful commits, a documented branching approach, pull requests, reviewer approval, and traceable links between work items and changes. Avoid shared-drive code, opaque copies, and direct production edits except through a documented emergency process.

CI/CD pipelines: Build and maintain pipelines that perform automated validation, package deployable artifacts, apply environment-specific configuration, and promote changes across development, test, and production. Use approvals, checks, service connections, protected environments, and release evidence appropriate to the risk of the change.

Automated testing: Create maintainable tests for Python and SQL logic, pipeline and notebook behavior, contracts and schemas, data-quality rules, reconciliations, and representative regression scenarios. Run fast checks on pull requests and broader integration or end-to-end checks before production promotion. Failed required tests must block the release or receive a documented exception.

Pay Range: $125,000.00-$145,000.00/year