APPLY - Senior Data Engineer
Requirements
• 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. • Snowflake (SnowPro) or dbt certification; cloud certifications welcome. • Docker and Kubernetes. • Experience with consumer or digital-product data (apps, games, subscriptions). • People are at the core of everything we do at APPLY. We provide you with modern tools, systems and approaches, value your time, safety, and health, and strive to build a work community where you can thrive and grow. Here are a few benefits we offer to support you: • Agentic Delivery: Our people work in a modern way to deliver client outcomes. Broaden your skills on a range of engagements with international brands that have a global impact. • An inclusive and safe environment: We’re truly committed to building a culture where you are celebrated and everyone feels welcome and safe. • AI & Strategic Upskilling: Accelerate your professional growth with generous training budgets and mentorship, with a specific focus on Agentic AI expertise and the critical human skills required for the future of work. • Generous vacation policy: Work-life balance is key to our team’s success, so we offer ample time away from work to promote overall well-being. • Flexible work arrangements: We work in a variety of ways, from remote, to in-office, to a blend of both. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Responsibilities
• 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.
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