Data Engineer, AI & Business Intelligence- San Diego

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 Dev Ops, 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.

• 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 Dev Ops: 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.

• 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 Dev Ops 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…