Senior Data Engineer

As a Senior Data Engineer, you will play a critical role in designing, building, and optimizing scalable data pipelines and architectures that support advanced analytics, machine learning, and business intelligence initiatives.

You will own the full path data takes through the platform: ingesting from source databases, third-party APIs, and vendor file feeds; landing and transforming it into governed warehouse models; and provisioning the underlying warehouse infrastructure as code. The platform spans on-premises and cloud source systems across a large, multi-domain estate (hundreds of source tables and transformation models) supporting business areas such as point-of-sale/box-office, loyalty, payments, and digital analytics.

You will collaborate closely with data scientists, analysts, and business stakeholders to ensure data is accessible, reliable, and actionable. This role requires a deep understanding of data engineering best practices, cloud platforms, and modern data stack technologies.

• Design, develop, and maintain robust, scalable, and efficient data pipelines
• Build and maintain SQL-based transformation models in a version-controlled analytics engineering framework (e.g. dbt), including incremental processing strategies and macros
• Architect and implement robust data models, dimensional models, and aggregate models.
• Provision and maintain warehouse infrastructure - databases, schemas, roles, and grants - as code
• Collaborate with enterprise business units to understand data requirements
• Optimize data systems for performance, scalability, and cost-efficiency, including compute sizing and cost/credit budget monitoring
• Ensure data quality and integrity through automated testing, source-to-target reconciliation, freshness monitoring, and documentation
• Implement and maintain performant CI/CD pipelines for data workflows
• Implement and maintain data governance and security controls, including role-based access, column-level data masking, and support for privacy/compliance requests
• Migrate and modernize legacy data and reporting systems onto current platform capabilities
• Mentor junior engineers, review peers' code, and contribute to team knowledge sharing and best practices
• Create comprehensive documentation of existing and planned data processes

• Bachelor's degree or above in Computer Science, Information Systems, or related science field, or equivalent practical experience
• 5+ years of experience as a Data Architect, Data Engineer, Analytics Engineer, or similar role
• Strong SQL skills and experience integrating relational source databases, on-premises and/or cloud-hosted (e.g. SQL Server, MySQL/Amazon RDS)
• Hands-on experience with a cloud data warehouse platform (e.g. Snowflake)
• Experience ingesting from non-relational sources - REST APIs, cloud analytics exports (e.g. Big Query/GA4), and vendor file feeds (e.g. SFTP, fixed-width/CSV)
• Proficiency in data modeling, ETL/ELT processes, and data warehousing solutions
• Experience with a general-purpose programming language and associated testing/code-quality practices (e.g. Python)
• Experience with a SQL transformation/analytics engineering framework (e.g. dbt)
• Experience with Python-based ingestion frameworks or managed connectors (e.g. dlt, Fivetran, Airbyte)
• Hands-on experience with cloud platforms (e.g. Azure - Blob/ADLS, Data Factory, Key Vault, Entra)
• Hands-on experience with orchestration tools (e.g. Airflow, Prefect)
• Hands-on experience with infrastructure as code tools (e.g. Terraform)
• Experience with version control and CI/CD (e.g. Git Hub Actions, Bitbucket Pipelines)
• Implementation experience with data governance, security, and compliance standards (e.g. RBAC, column-level masking, PII handling)
• Excellent analytical, problem-solving, and written communication skills - able to document decisions and trade-offs clearly for technical and non-technical audiences
• Ability to work cross-functionally with technical and non-technical teams

• Experience with hybrid connectivity between cloud and on-premises networks (e.g. VPN/VNet, private endpoints, DNS forwarding)
• Experience with CDP, reverse-ETL, or audience activation tooling (e.g. Rudder Stack, Braze, Google Ads Customer Match)
• Experience with BI platform security models (e.g. Power BI row-level security, Sigma)
• Familiarity with data catalog and lineage tooling (e.g. Data Hub)
• Experience migrating off legacy ETL/BI stacks (e.g. SSIS, SSAS, SQL Server Agent)