Senior Data Engineer - AI

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 versatile Senior 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. Applicants 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 Qualifications 7+ years of dedicated 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, Scala, or Java. Strong background in modern software development practices (testing, code review, CI/CD, Infrastructure as Code). Preferred Qualifications Extensive, progressive experience leading technical projects and mentoring engineering teams. Hands-on experience with cloud-native infrastructure (AWS, GCP, or Azure). Deep understanding of dimensional data modeling and warehouse optimization techniques. Experience implementing data observability, monitoring, and alerting frameworks at scale. Background in enterprise software, planning, or financial analytics applications. Familiarity with Anaplan or similar enterprise planning platforms. What Makes This Role Exciting Lead high-impact projects, building scalable data architectures from the ground up. Work on cutting-edge Big Data technologies and the systems that power modern AI infrastructure. Build foundational data platforms that directly impact how global enterprises plan and make critical business decisions. Tackle unique scaling challenges dealing with massive, complex enterprise datasets. Collaborate with talented software engineers, product managers, and analytics experts. Solve highly complex challenges at the intersection of big data infrastructure and enterprise software. #LI-SP1 Our Commitment to Diversity, Equity, Inclusion and 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