Data Engineer

TAIT partners with artists, brands, IP holders and place makers to bring culture-defining, never-before-seen experiences to life.

With a legacy of innovation spanning over 45 years, TAIT has grown from pioneering in rock ‘n’ roll concert staging to setting the global standard for extraordinary live events and experiences through cutting-edge technology, precision engineering, and creative design. TAIT’s 20 global offices have developed iconic productions and experiences in over 30 countries, all seven continents, and even outer space for renowned performers, theme parks, exhibits, and venues across the globe, including partnerships with Taylor Swift, Cirque Du Soleil, Royal Opera House, Nike, NASA, Bloomberg, Google, Beyoncé, and The Olympics

TAIT is looking for a hands-on Data Engineer to build, scale, and operate the governed data platform that will underpin the next phase of the company's technology transformation. Reporting to the Data Architect, this role will turn the target data architecture into reliable pipelines, reusable data products, trusted analytical models, and production platform capabilities.

This role is ideal for an experienced engineer who can move between architecture and execution and who has a proven track record delivering production-grade Databricks solutions in AWS. The successful candidate will do more than move data from one system to another: they will have delivered projects that combine complex enterprise and operational data to enable sophisticated analysis, forecasting, optimization, AI/ML use cases, and confident executive decision-making.

The Data Engineer will work closely with the Data Architect and partner across Enterprise Technology, Architecture/AI, Integration, Infrastructure and Cloud, Information Security, and business teams including Finance, Sales, Project Delivery, Operations, Procurement, Manufacturing, and People/Resourcing. The role will help establish clear sources of truth and enable analytics across systems such as Salesforce CRM and CPQ, Kantata PSA, Epicor and the next-generation ERP, MES, Autodesk/PLM, HCM, service management platforms, and other internal and external data sources.

• Design, build, deploy, and operate production data pipelines and lakehouse capabilities using Databricks on AWS.
• Implement scalable ingestion, transformation, orchestration, and serving patterns for batch, near-real-time, streaming, API, file, and change-data-capture workloads.
• Use AWS services and controls - including S3, IAM, KMS, VPC networking, Secrets Manager, Cloud Watch, and appropriate catalog or integration services - to create secure and supportable data solutions.
• Develop reliable data layers, optimizing workloads, storage design, cluster or serverless configuration, performance, and platform cost.
• Establish automated deployment practices using Git, CI/CD, infrastructure as code, automated testing, and environment promotion across development, test, and production.
• Implement monitoring, observability, alerting, recovery, service-level objectives, and operational runbooks for critical data products and pipelines.

• Translate high-value business questions into well-defined data products, curated datasets, and semantic-ready models for Power BI, advanced analytics, and AI-enabled solutions.
• Integrate data from multiple enterprise and operational sources to support strategic analytics, dashboards, KPIs, project and customer profitability, resource and capacity analysis, financial planning, forecasting, scenario modeling, operational optimization, and other sophisticated analytical use cases.
• Partner with analysts, product owners, and business subject-matter experts to ensure data products are analytically fit for purpose, clearly defined, and adopted by their intended users.
• Create reusable datasets and feature-ready data that accelerate experimentation and responsible delivery of machine learning, generative AI, and other advanced analytical capabilities.
• Define and track technical and business measures for data-product adoption, quality, timeliness, reliability, and…