Engineer III - Data Engineer

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! What you'll do • Data Pipeline & Orchestration: Design, build, and run batch and scheduled data pipelines (Airflow, Dagster, Prefect) and lead orchestrator upgrades. • Data Ingestion & Scraping: Own and extend data collectors and engines, including web/API ingestion, large-scale web scraping (Playwright, Selenium, Scrapy), parsing, normalization, and file merging. • Distributed Processing: Build scalable distributed processing architectures (Spark on EMR, Data bricks, Data proc) and analytical query layers (Athena, Snowflake, BigQuery), including cross-cloud ingestion pipelines. • Caching & Entity Matching: Improve entity matching and caching services (Redis, MemoryDB, DynamoDB) to optimize accuracy, latency, and operational cost. • Data Access & APIs: Develop and maintain high-performance Python data access layers and APIs for downstream ML and product teams. • Data Quality & Observability: Own data quality initiatives, including validation frameworks, retention policies, backfills, monitoring, and OpenTelemetry-based observability. • Cross-Functional Collaboration: Partner with data science, forecasting, and platform teams to ship features safely behind feature flags (LaunchDarkly, Unleash). • Engineer ing Leadership: Raise engineer ing standards across the team through rigorous code reviews, technical design documents, and mentoring. What you bring • Experience: 6+ years of software or data engineer ing experience, including 3+ years of experience building and maintaining production data pipelines. • Python Mastery: Strong Python development skills with a commitment to writing clean, tested code (using pytest, nox, or tox). • Orchestration Expertise: Deep experience with workflow orchestration (Airflow DAG design, operators, scheduling, and failure handling) or modern alternatives like Dagster and Prefect. • Data Ecosystems: Hands-on experience with distributed compute (Spark) and cloud data ecosystems (AWS EMR/Athena/S3, Data bricks, Snowflake, GCP). • Storage & Modeling: Solid SQL and data -modeling skills, alongside practical experience with NoSQL data bases, caching layers, and message queues (MongoDB, Redis, Kafka, RabbitMQ). • Infrastructure Familiarity: Comfort deploying and managing containerized workloads on Kubernetes using Docker and Helm. • Production Ownership: A proven track record of owning production pipelines, including participating in on-call rotations, incident response, and root-cause analysis. • Communication: Clear written technical communication and a history of documenting architectural decisions. Nice to have • Scraping Infrastructure: Experience managing large-scale web data collection and distributed crawler infrastructure. • Data Science / M