Work with internal disciplines, vendors, clients and stakeholders to define data requirements and the technology stack for projects.
Audit source systems (CRM and marketing platforms such as Salesforce and Sailthru) and define migration guidelines: what to migrate, in what structure and in what order, for execution by internal or third-party teams.
Translate business requirements into technical specifications including data streams, integrations, transformations, databases and data warehouses.
Design and build transformation layers in dbt on Snowflake, including identity resolution, semantic layer and consumption/reporting layers.
Build and maintain pipelines that activate data in customer engagement platforms (e.g., Braze).
Run discovery audits of digital products (apps, games, web) and produce data foundation recommendations.
Work in git-based workflows with code review, dbt tests and existing CI/CD processes for warehouse objects.
Define the data architecture framework, standards and principles, including modelling, metadata, security and reference data; analyze and optimize existing architectures and models for performance and cost.
Create a plan of activities required to populate new solutions with all current and historical data.
Implement data security and access controls to ensure compliance with company policies and regulation.
Document data sources, ELT/ETL processes, data dictionaries and metadata to facilitate data discovery; maintain data lineage and data flow documentation.
Research, evaluate and recommend new technologies, and develop best practices for data management, security and privacy.
A Bachelor's degree in Computer Science, Information Systems, or a related field.
At least 5 years of experience in data engineering or related fields.
Expert-level SQL and strong experience in ELT/ETL and cloud data warehousing, with hands-on Snowflake experience.
Strong hands-on experience with dbt (modelling, tests, documentation, macros) and git-based workflows (branching, code review, CI/CD).
Proven experience in data modelling (dimensional and semantic layers) and analysis techniques.
Experience with customer data modelling and identity resolution across CRM, marketing and product sources.
Experience integrating data with customer engagement platforms (Braze, Salesforce Marketing Cloud, Sailthru or similar).
Experience planning data migrations between platforms: source audit, mapping, target structure definition and historical backfill.
Working knowledge of Python for data tooling and automation.
Working knowledge of at least one major cloud platform (AWS or GCP) and its data services.
Demonstrated ability to troubleshoot data issues and develop effective solutions.
Comfortable producing written guidelines and recommendations for client and third-party teams.
Understanding of data privacy and security best practices.
Excellent communication and problem-solving skills.
Fluent English.
Nice to Have
Snowflake (SnowPro) or dbt certification; cloud certifications welcome.
Spark/PySpark.
Docker and Kubernetes.
Experience with consumer or digital-product data (apps, games, subscriptions).