Staff Data Engineer

Iterable is the AI customer engagement platform, built for enterprise scale, loved by teams, and trusted by global brands like Redfin, SeatGeek, Priceline, Calm, and Box. Our platform empowers organizations to activate customer data from any source, design seamless cross-channel experiences, and optimize engagement with Nova Intelligence, the AI layer of our platform, all with enterprise-grade security and compliance. Today, nearly 1,200 brands across 50+ countries rely on Iterable to drive growth, deepen customer relationships, and deliver joyful customer experiences. Our success is powered by extraordinary people who bring our core values to life every day: Be an Owner, Growth Mindset, Run as One, and Transparency. And the market has noticed: G2 recognizes Iterable as a Leader globally, spanning the Americas, EMEA, APAC, ANZ, and Latin America, with category leadership in Personalization, Marketing Automation, Push Notifications, and Mobile Marketing, and a 4.5/5-star rating from hundreds of users. Gartner named Iterable a Challenger in the Gartner Magic Quadrant for Multichannel Marketing in both 2024 and 2025. We’ve also earned TrustRadius Top Rated honors in 2024 and 2025, were named a TrustRadius Buyer’s Choice for 2026, and were featured in Snowflake’s Modern Marketing Data Stack 2026. With a global presence that includes offices in San Francisco, Denver, London, Sydney, and Lisbon, plus remote employees worldwide, we are committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and encourage you to apply. Learn more about our story and mission on our Culture https://iterable.com/culture/ and About Us https://iterable.com/company/ pages. Let’s shape the future of customer engagement together! How you will make an impact: As a Staff Data Engineer at Iterable, you'll build reliable data pipelines and platform capabilities that power customer-facing data movement, analytics data products, and machine learning foundations. You will lead large-scope or high-impact projects, drive technical decisions through ambiguity, and collaborate across engineering, product, infrastructure, and data science to deliver scalable, observable systems. You will help evolve our ingestion, activation, analytics, and ML data workflows while improving reliability, data quality, operational visibility, and long-term maintainability across the platform. How you will make a difference: - Own the architecture and end-to-end delivery of critical workflows in our next-generation data ingestion and activation platform, including source connectors, staging and transformation workflows, diffing and incremental sync logic, and integrations with Iterable bulk APIs. - Define and apply high-availability, fault-tolerance, and recovery patterns for data-intensive systems, including graceful degradation, retries, replayability, backfills, idempotency, and durable workflow execution. - Lead the design of Snowflake-based data pipelines and data-sharing workflows that improve freshness, correctness, scalability, and operability for analytics products and customer-facing data delivery. - Establish technical standards for schema evolution, data quality, observability, testing, and lifecycle management so data systems are easier to extend, debug, and support at scale. - Partner with data science and machine learning engineers to architect robust data infrastructure behind feature generation, feature serving, model inputs, and experimentation workflows. - Drive technical tradeoffs across batch and streaming patterns, raw-to-curated data modeling, latency, performance, cost, availability, and maintainability as systems scale. - Collaborate with cross-functional partners across product, frontend, platform, SRE, and customer-facing teams to define interfaces, resolve complex production issues, and align solutions with broader platform direction. - Identify architectural gaps and operational risks early, and turn them into concrete roadmaps, design proposals, and execution plans. - Create reusable abstractions, platform capabilities, and engineering patterns that raise the leverage of the broader team beyond immediately owned systems. - Provide technical leadership through design reviews, implementation guidance, incident reviews, and mentorship for senior and mid-level engineers. - Influence roadmap and prioritization discussions by connecting platform investments to reliability, scalability, and long-term engineering efficiency. We are looking for people who have: - 8+ years of relevant experience in data engineering, software engineering, or adjacent platform and infrastructure roles. - Deep experience designing and operating production data pipelines, ETL/ELT systems, or data platforms at scale. - Strong proficiency in Python and SQL, plus familiarity with at least one additional programming language such as Scala or Java. - Experience with modern data tooling and cloud platforms such as Snowflake, S3, Databricks, Postgres, Redis, or similar systems. - Strong experience with orchestration and workflow concepts, and with designing batch and/or streaming data systems. - Deep understanding of data modeling, schema evolution, incremental processing, testing, observability, and operational excellence for reliable data products. - A track record of owning ambiguous, high-impact technical initiatives from problem definition through adoption and operational maturity. - Experience designing for high availability, resilience, and recovery in systems with strict reliability or customer delivery expectations. - Experience influencing technical direction across teams and building alignment without direct authority. - Desire to work in a highly remote/distributed but collaborative environment. - Willingness to participate in operational support or on-call as needed. - Fluency in English (verbal and written). - Legally authorized to work in the EU. Our Technology Stack: - Progra