Sr. Engineer - IT Data Analytics

Wynn Resorts•Las Vegas, NV

About The Position

Wynn Resorts is building an integrated system of data, analytics, and action to attract guests and scale personalized service, driving loyalty, visit frequency, and revenue. The Senior Data Engineer is a hands-on senior engineer on the Customer Intelligence Platform (CIP) team, responsible for designing, building, and operating cloud data pipelines and models that turn guest, gaming, and hospitality data into trusted, real-time knowledge for the business. The role contributes engineering input to solution designs, implements physical data models, builds reusable batch and streaming ingestion patterns, and integrates hospitality and gaming source systems. It also builds pipelines as code with CI/CD and automated testing, and supports production monitoring, incident response, and cost and performance tuning. The engineer implements data quality, lineage, observability, governance, access control, and PII protection measures. The role also builds data foundations for predictive ML, LLMs, and AI agents; applies automation to data operations; and uses AI-assisted development tools while maintaining quality, security, and review standards. The Senior Data Engineer mentors other engineers, champions engineering standards, and translates stakeholder requirements into technical designs and delivery plans.

Requirements

At least six years of data engineering experience, including at least three cloud data analytics projects delivered end to end.
Production experience building and operating data pipelines on Microsoft Azure, Snowflake, and Databricks.
Advanced SQL skills, including complex transformations, window functions, and performance tuning on large datasets.
Advanced Python for data engineering, including PySpark, Snowpark, automated testing, and reusable packaged code.
Hands-on experience implementing dimensional and medallion data models in a cloud warehouse or lakehouse.
CI/CD and Git-based workflows for data pipelines, such as Azure DevOps.
Regular hands-on use of AI coding assistants in production engineering work, with sound practices for reviewing, testing, and securing AI-generated code.

Nice To Haves

Experience in the casino resort, hospitality, or luxury industries, including guest identity resolution or customer data unification.
Orchestration and streaming experience with Airflow and Kafka.
Erwin or comparable modeling tools, plus infrastructure as code such as Terraform.
Governance tooling such as Databricks Unity Catalog or Microsoft Purview.
MLOps, feature stores, vector search, or LLM and AI agent data pipelines.
Certifications in Snowflake, Databricks, or Microsoft Azure.

Responsibilities

Contribute engineering input on feasibility, performance, and cost during solution design sessions involving Snowflake, Azure Databricks, and the Azure data platform.
Implement physical data models from approved logical designs, including dimensional, medallion, and canonical patterns, and deploy them as production schemas in Snowflake and Databricks.
Support evaluation of ingestion, integration, replication, and open table format options through proofs of concept and benchmarking.
Document data flows, technical specifications, and data contracts for downstream teams.
Define, design, and implement reusable batch and streaming ingestion patterns using Databricks, Airflow, Kafka, and Snowflake.
Deliver real-time and near-real-time guest and operational data to the business.
Integrate hospitality and gaming source systems, including property management, player tracking, CRM, and marketing platforms.
Build pipelines as code with CI/CD, automated testing, and promotion across development, test, staging, and production environments.
Maintain production ingestion tooling, including monitoring, alerting, incident response, and cost and performance tuning.
Implement data quality controls, including profiling, cleansing, validation rules, and automated checks embedded in pipelines.
Build lineage, reconciliation, and observability into pipelines to maintain data integrity from source to consumption.
Implement platform governance standards, including catalog registration, access control, and classification.
Implement PII protection controls, including masking, least-privilege access, and audit logging, to meet privacy and gaming regulatory requirements.
Build data foundations for predictive ML, LLM, and AI agent use cases, including feature pipelines and retrieval-ready datasets.
Apply automation, including RPA and AI agents, to streamline data operations and analytics delivery.
Use AI-assisted development tools to accelerate pipeline development, testing, and documentation while maintaining code quality, security, and review standards.
Guide and mentor data engineers through code reviews, pairing, and design feedback.
Set and champion engineering standards, reusable frameworks, and best practices across the team.
Translate requirements from Marketing, Gaming, Hotel, and Analytics stakeholders into technical designs and delivery plans.