Principal Data Engineer

Principal Data Engineer Overview

Anaplan is looking for a Principal Data Engineer to help shape the future of its data platform in London. This role sits at the center of the company’s data strategy and is focused on building the systems that allow data to be ingested, transformed, stored, served, and governed at very large scale. The work supports business planning workflows, advanced analytics, and AI initiatives, so the position calls for someone who combines deep distributed systems knowledge with strong software engineering discipline and a track record of delivering reliable, high-performance data solutions.

The company describes itself as a team of innovators working to improve business decision-making through an AI-enabled scenario planning and analysis platform. Its customers include major global organizations, and the culture is presented as one that values customer success, ambitious goals, leadership at every level, and celebration of wins. Diversity of thought, inclusive collaboration, and disciplined execution are presented as core operating principles.

What you will do

• Define the technical direction for the full data platform, including how data is brought in, transformed, stored, delivered, and governed.
• Design and implement robust, high-throughput data pipelines that can process massive datasets in both real-time and batch modes.
• Lead the architecture, design, and production deployment of scalable big data systems.
• Build and manage the core data infrastructure that supports modern AI capabilities, including vector databases, NoSQL systems, and document-oriented stores.
• Create complete data engineering solutions from end to end, including ETL and ELT pipelines, API services, and ingestion frameworks.
• Develop the storage and processing foundations for analytics workloads, such as data lakes, data warehouses, distributed file systems, and streaming architectures.
• Engineer context-aware pipelines that can handle enterprise-scale data while blending batch and streaming patterns effectively.
• Improve the performance of distributed queries and data transformations so users experience low latency and high responsiveness.
• Establish data quality mechanisms that help measure, protect, and improve integrity, reliability, and governance across all data assets.
• Partner with analytics, product, and platform teams to design data models that accurately represent customer metrics, hierarchies, and relationships.
• Evaluate emerging tools, frameworks, and database technologies in the modern data and big data ecosystem to determine whether they should be adopted.
• Provide technical leadership on complex initiatives and contribute to the growth of the engineering function through mentoring where appropriate.

What the role requires

• Extensive experience in data engineering with a strong history of hands-on delivery in complex, high-pressure environments.
• Deep practical knowledge of database technologies used in AI and machine learning infrastructure, including vector databases, NoSQL databases, and document stores.
• Proven experience building, scaling, and shipping large production data platforms.
• Strong working knowledge of distributed processing frameworks such as Apache Spark, Flink, or Hadoop.
• Substantial experience with messaging and event streaming technologies such as Apache Kafka or Kinesis.
• End-to-end experience across the data pipeline lifecycle, including orchestration tools such as Apache Airflow or Dagster.
• Hands-on experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift, as well as data lake technologies such as Databricks, Delta Lake, or Apache Iceberg.
• Advanced SQL skills and strong proficiency in Python.
• A solid background in modern engineering practices, including testing, code review, continuous integration and delivery, and infrastructure as code.
• Preferred experience includes leading technical projects and mentoring engineers over time.
• Preferred experience includes working with cloud-native infrastructure on AWS, GCP, or Azure.
• Preferred experience includes implementing observability, monitoring, and alerting for data systems at scale.
• Preferred experience includes familiarity with Anaplan or a comparable enterprise planning platform.

Culture, inclusion, and support

Anaplan states that it is committed to diversity, equity, inclusion, and belonging, and that these principles are central to how the company innovates, collaborates, and succeeds. The company says it welcomes people of all backgrounds and identities and aims to create a workplace where everyone is respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or other personal characteristics.

The company also says it will provide reasonable accommodations for applicants and employees with disabilities so they can take part in the hiring process, perform the essential duties of the role, and receive equal employment benefits and privileges.

Additional information

The posting includes a fraud warning explaining that Anaplan does not make offers without a substantial interview process involving recruitment and hiring managers, does not send job offers by email, and uses @anaplan.com email addresses for official communication. If there is any doubt about the authenticity of a message or call claiming to be from Anaplan, candidates are instructed to contact the company by email before taking further action.

The application is submitted through Greenhouse and asks candidates to provide standard personal, contact, and employment details, along with a resume and LinkedIn profile. It also asks whether the applicant is legally authorized to work in the country where the role is based and whether they now or in the future will require visa sponsorship for employment at Anaplan.

The role is listed for London, United Kingdom.

Degree Requirement: Not Specified

Visa Sponsorship May be