AI Data Platform Engineer

About the role

Hi, we are Storyteq! 👋🏼 Storyteq helps marketing and creative teams in top brands to scale their creative production and gain control over their marketing campaigns. Through our platform, our clients can streamline campaign workflows, automate their creative production through templates & AI and activate engaging campaigns that go to market faster. We believe creativity takes time, but creative production shouldn't. Since 2016 our mission has been to revolutionize the way creative assets are created and used. Magic happens when we let technology empower creativity. Our endless curiosity and relentless commitment to our customers lies at the heart of our problem-solving approach. This shared mission is woven into our values: we dream big, think differently, and are stronger together. Role Overview The Senior Data Platform Engineer is a hands-on technical specialist responsible for implementing and maintaining the data infrastructure that underpins our global platform. Working across a range of database technologies, from relational databases through to vector databases, graph databases, and cloud-scale data lakes, this engineer builds reliable, high-quality data systems that meet the needs of our product and engineering teams. A key part of this role is contributing to the build-out of an enterprise-scale data layer that provides rich, reliable context to our agentic AI product capabilities. As we advance our AIpowered platform, the quality, structure, and accessibility of our underlying data is critical, and this engineer will play an important hands-on role in ensuring that foundation is sound. You will work closely with product and engineering teams to understand data requirements, contribute to technology decisions, and implement solutions that are well-configured, clearly documented, and built to last. This is a role for an engineer who takes real pride in the craft of data engineering and who cares about the reliability and integrity of the systems they build. 1. Database Implementation & Technology Selection Work with product and engineering teams to understand data requirements and contribute to technology selection, covering relational databases, vector databases, graph databases, and cloud data lake technologies. Implement chosen database technologies, including configuration, schema design, and integration with application and data layers. Apply data modelling best practices to ensure schemas are well-structured, performant, and maintainable. Ensure configurations are optimised for performance, reliability, and cost efficiency, and keep documentation of schemas and architectural decisions up to date 2. Data Integrity & Consistency Implement validation, constraint, and reconciliation mechanisms to prevent and detect data corruption or inconsistency across distributed data stores. Apply established best practices for data integrity and consistency, including idempotency and conflict resolution patterns in distributed systems. Monitor data health and address integrity issues promptly before they impact product or customer outcomes. Work with application engineers to ensure data quality considerations are factored into how services write and consume data. 3. Data Lake Engineering & ETL Pipelines Build and maintain scalable data lake architectures on Google Cloud Platform including data modelling, partitioning, clustering, and cost optimisation. Develop robust ETL/ELT pipelines that ingest, transform, and serve data reliably at scale. Instrument pipelines with appropriate observability, error handling, retry logic, and lineage tracking. Ensure data lake structures are well-organised and accessible to support analytics, data science, and AI product use cases. 4. Data Quality & Governance Build and maintain automated data quality checks, monitoring, and alerting across pipelines and data stores. Work with data consumers, including data science, analytics, and product teams, to understand quality requirements and address issues as they arise. Apply data governance practices in day-to-day work, including appropriate access controls, retention handling, and lineage tracking. Ensure sensitive data is handled correctly and in line with security and compliance requirements. Experience & Skills Required Solid hands-on experience with relational database technologies including schema design, query optimisation, indexing, and day-to-day operational management. Practical experience with vector databases and vector search technologies (e.g. Pinecone, Weaviate, pgvector, Vertex AI Search) in production or near-production environments. Working knowledge of graph database technologies (e.g. Neo4j, Spanner Graph) and an understanding of when they are the right tool for the job. Hands-on experience building and operating data lakes on Google Cloud Platform, with strong BigQuery skills including data modelling, partitioning, clustering, and cost management. Familiarity with the broader Google Cloud data ecosystem, incl