Senior Data Engineer

Las Vegas Sands•Dallas, TX

About The Position

The Senior Data Engineer designs, builds, and maintains data pipelines, lakehouse models, and analytics data services for casino management systems. The role delivers scalable, reliable, high-performance data solutions within a modern Iceberg Data Lakehouse architecture supporting real-time processing, analytics, and reporting. Working under the guidance of the Lead Data Engineer and reporting to the Director – Integration & Data, the engineer owns significant platform components and partners with the Data Portal and cross-functional application teams to ensure seamless integration and data flow across systems. Duties are performed in accordance with departmental and Las Vegas Sands Corp. policies, practices, and procedures, and Team Members are expected to maintain professional conduct and observe company standards and work requirements. The position includes developing and maintaining Iceberg lakehouse components, Kafka-based pipelines, Dremio data models and semantic layers, and a data access layer for downstream consumers. It also involves integrating diverse data sources, supporting batch and real-time analytics, maintaining PostgreSQL schemas and queries, applying data governance practices, collaborating with analysts, scientists, and business stakeholders, and monitoring and optimizing performance. The engineer mentors other engineers, uses Claude Code with appropriate review of AI-generated output, documents data assets and workflows, follows safety practices, attends work regularly as scheduled, and performs other related duties as assigned.

Requirements

Be at least 21 years of age.
Provide proof of authorization to work in the United States.
Hold a Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
Be able to obtain and maintain any certification or license required by law or policy.
Have 5+ years of data engineering experience, including demonstrated ownership of end-to-end production pipelines.
Have strong hands-on experience with Apache Kafka for real-time event streaming and data pipeline development.
Have hands-on experience with a modern distributed SQL query engine such as Dremio, Starburst/Trino, or StarRocks, for analytics, reporting, and query federation across heterogeneous data sources.
Have demonstrated experience building semantic layers, reflections, and curated views in Dremio or a similar tool for reporting and self-service analytics.
Have strong working knowledge of PostgreSQL 16 or later, including query optimization, indexing strategies, and schema design for application and analytical workloads.
Have demonstrated experience with Iceberg catalog management, such as Apache Polaris or Lakekeeper, and object storage, such as AWS S3 or MinIO.
Have familiarity with cloud data solutions such as AWS or Azure and deploying data services on Kubernetes using Docker containers.
Have strong Python 3.11 or later programming skills for data pipelines, ingestion frameworks, and processing workflows, and familiarity with PyIceberg, PyArrow, and Pydantic.
Have solid experience with data modeling techniques such as star and snowflake schemas for analytics and reporting.
Have demonstrated experience with data quality frameworks and tools to ensure data integrity and compliance with business standards.
Have working knowledge of CI/CD practices using GitHub Actions for automated build, test, and deployment processes.
Have experience with monitoring and observability tools such as Prometheus and Grafana.
Have strong analytical and problem-solving abilities focused on delivering high-quality data solutions.
Have strong interpersonal skills and the ability to communicate and interact appropriately with management, Team Members, and outside contacts of different backgrounds and experience levels.
Be able to physically access assigned workspace areas, with or without reasonable accommodation.
Be able to work indoors and be exposed to environmental factors including CRT, noise, and dust.
Be able to use a laptop and standard keyboard to perform essential job functions.

Nice To Haves

Experience in the gaming or casino industry.
Familiarity with Apache Iceberg, including partitioning strategies, schema evolution, and snapshot management.
Dremio experience.
Experience mentoring less experienced engineers and contributing to knowledge sharing and continuous improvement.
Familiarity with BI tools such as Metabase, Tableau, or Power BI.

Responsibilities

Design, build, and maintain Iceberg Data Lakehouse components using Apache Iceberg, Dremio, and Apache Polaris, contributing to architectural decisions about scalability, security, and extensibility.
Develop and maintain Python and Apache Kafka data pipelines for ingestion, transformation, and storage in Iceberg tables, ensuring availability and data quality.
Build and maintain curated data models and a Dremio semantic layer for dashboards, reports, self-service analytics, and scheduled exports.
Build and maintain a data access layer that serves lakehouse data to downstream consumers, including JSON and CSV exports for use cases such as regulatory reporting.
Integrate transactional systems, third-party APIs, IoT devices, and other sources through Kafka event streaming and Dremio query federation.
Implement ingestion and transformation processes in a Lambda-Kappa hybrid architecture for batch and real-time analytics.
Develop and maintain PostgreSQL schemas and queries for application state, metadata management, and analytical workloads.
Apply and help enforce data governance policies and practices using Dremio and Polaris capabilities to support regulatory compliance, security, and privacy.
Collaborate with data analysts, data scientists, and business stakeholders to understand requirements and deliver solutions, including embedded Metabase dashboards and datasets for portal features.
Monitor and optimize pipeline performance using Prometheus and Grafana, identify bottlenecks, and improve processing and retrieval.
Mentor junior and mid-level data engineers through code review, pairing, and knowledge sharing.
Use Claude Code for pipeline development and refactoring, testing, and codebase documentation; review and verify AI-generated output before production use.
Maintain documentation of data models, data contracts, workflows, and processes.
Perform job duties safely, attend work consistently and regularly as scheduled, and perform other related duties as assigned.