Principal Data Platform Architect for AI-Driven Analytics

Anaplan

Greater London

On-site

GBP 120,000 - 180,000

Full time

8 days ago

Get more replies from employers

Send a job-specific resume in minutes.

Job summary

Anaplan is seeking a Principal Data Engineer to shape the data platform across ingestion, transformation, storage, and governance at scale. You will build high‑throughput pipelines powering AI initiatives and analytics, enabling business users to act on massive datasets in planning workflows.

You will lead architecture and implementation of data lakes, warehouses, vector databases, and streaming systems, partnering with analytics, product, and platform teams to deliver reliable, scalable data

Qualifications

Extensive data engineering experience in complex data environments.
Hands-on with AI/data platforms and scalable pipelines.
Experience with distributed processing and cloud data warehouses.

Responsibilities

Lead the data architecture, design, and deployment of scalable, high-throughput Big Data systems into production environments.
Architect, deploy, and manage the foundational data systems that underlie modern AI infrastructure, including vector, NoSQL, and document databases.
Develop end-to-end data engineering solutions, including robust ETL/ELT pipelines, API services, and data ingestion frameworks.
Design and build the storage and processing layers powering our analytics workloads: data lakes, data warehouses, distributed file systems, and real-time streaming architectures.
Engineer feature-rich context pipelines that process large-scale enterprise data, balancing batch and streaming patterns seamlessly.
Optimise and scale large distributed queries and data transformations to ensure high performance and low latency for end users.
Implement data quality frameworks to measure and ensure data integrity, reliability, and governance across all data assets.
Collaborate with analytics, product, and platform teams to build data models that capture the semantics of customer metrics, hierarchies, and relationships.
Stay current with the modern data stack and big data landscape, evaluating new tools, distributed computing frameworks, and database technologies for potential adoption.