Principal Data Engineer - AI

Anaplan; Pennsylvania; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Anaplan is seeking a Principal Data Engineer to lead the design and deployment of scalable data platforms across our data lake/warehouse ecosystem. You will build robust pipelines, govern data, and enable real-time and batch analytics for AI initiatives.

You will collaborate with analytics and product teams, drive data quality, and stay current with modern data stack innovations. This hybrid role is open to Eastern/Central time zones with onsite two days weekly in the PA region.

Qualifications

• Extensive data engineering experience in complex data environments.
• Strong understanding of AI/ML data foundations and vector databases.
• Hands-on experience building large-scale data platforms in production.
• Proficient with distributed processing frameworks (Spark, Flink, Hadoop).

Responsibilities

• Lead data architecture, design, and deployment of scalable, high-throughput Big Data systems into production environments.
• Architect, deploy, and manage foundational data systems for AI infrastructure including vector/noSQL/document stores.
• Develop end-to-end data engineering solutions with robust ETL/ELT pipelines, APIs, and ingestion frameworks.
• Design storage/processing layers for analytics workloads: data lakes, warehouses, distributed file systems, real-time streams.
• Engineer feature-rich data pipelines handling large-scale enterprise data.
• Optimize and scale large distributed queries and data transformations for low latency.
• Implement data quality frameworks for data integrity and governance.
• Collaborate with analytics, product, and platform teams to model customer metrics and relationships.

Skills

Big Data systems

Distributed computing

SQL

Python

Apache Spark

NoSQL/Document stores

Data pipelines

Cloud data warehouses

CI/CD

Data governance

Education

Bachelor's in Computer Science or related

MS in Data Engineering or related

Tools

Apache Kafka

Snowflake

BigQuery

Redshift

Databricks

Airflow

Dagster

Kinesis

Job description

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.

What unites Anaplanners across teams and geographies is our collective commitment to our customers' success and to our Winning Culture.

Our customers rank among the who's who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.

Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebrating our wins - big and small.

Supported by operating principles of being strategy-led, values-based and disciplined in execution, you'll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!

We're seeking a Principal Data Engineer who can work across the full stack of Anaplan's data platform, setting the technical direction for how we ingest, transform, store, serve, and govern data at scale. You will build highly performant, robust data pipelines that process massive volumes of data in real-time and batch. This foundational work empowers business users to leverage vast datasets in their planning workflows and forms the bedrock for our advanced analytics and AI initiatives. You'll need deep knowledge of distributed computing, data architecture, and strong software engineering skills to tackle complex, high-scale data challenges.

This role is open to candidates located in the Eastern or Central time zones. Employees who live within commuting distance of one of our offices will be expected to work onsite two days per week as part of our hybrid work model

Your Impact

• 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.


• Optimize 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.

Your Skills

• Extensive data engineering experience, demonstrating a strong track record of hands‑on execution and delivery in complex data environments.


• Deep practical understanding of the database ecosystems that power AI and machine learning infrastructure (e.g., Vector databases, NoSQL, and Document stores).


• Hands‑on experience building, scaling, and shipping large‑scale data platforms in production.


• Deep practical experience with distributed data processing frameworks (e.g., Apache Spark, Flink, Hadoop).


• Strong expertise in message brokers and event streaming platforms (e.g., Apache Kafka, Kinesis).


• End‑to‑end exposure to data pipeline lifecycle development, including extensive experience with workflow orchestration tools (e.g., Apache Airflow, Dagster).


• Hands‑on expertise with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift) and data lake architectures (e.g., Databricks, Delta Lake, Apache Iceberg).


• Advanced SQL skills and proficiency in Python.


• Strong background in modern software development practices (testing, code review, CI/CD, Infrastructure as Code).

Desirable

• Extensive, progressive experience leading technical projects and mentoring engineering teams.


• Hands‑on experience with cloud‑native infrastructure (AWS, GCP, or Azure).


• Experience implementing data observability, monitoring, and alerting frameworks at scale.


• Familiarity with Anaplan or similar enterprise planning platforms.

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Our Commitment to Diversity, Equity, Inclusionand Belonging (DEIB)

We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren’t just words on paper – this is what drives our innovation, it’s how we connect, and it contributes to what makes us a market leader. We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day!

We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive equitable benefits and all privileges of employment. Please contact us to request accommodation.