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Salary: EUR 82,500 - 108,000 / annual
Iterable is an AI-powered customer engagement platform trusted by global brands like Redfin, SeatGeek, Priceline, Calm, and Box. The company serves nearly 1,200 brands across 50+ countries and is recognized as a Leader by G2 and a Challenger by Gartner in the Multichannel Marketing space.
As a Staff Data Engineer, you will architect and deliver critical data infrastructure that powers customer-facing data movement, analytics products, and machine learning foundations. This is a high-impact individual contributor role with significant technical leadership scope.
Key responsibilities include:
- Owning the architecture and end-to-end delivery of critical workflows in the 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.
- Defining and applying high-availability, fault-tolerance, and recovery patterns for data-intensive systems, including graceful degradation, retries, replayability, backfills, idempotency, and durable workflow execution.
- Leading 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.
- Establishing technical standards for schema evolution, data quality, observability, testing, and lifecycle management across the organization.
- Partnering with data science and ML engineers to architect robust data infrastructure for feature generation, feature serving, model inputs, and experimentation workflows.
- Driving technical tradeoffs across batch and streaming patterns, raw-to-curated data modeling, latency, performance, cost, availability, and maintainability.
- Collaborating with cross-functional partners across product, frontend, platform, SRE, and customer-facing teams to define interfaces and resolve complex production issues.
- Identifying architectural gaps and operational risks early, turning them into concrete roadmaps and execution plans.
- Creating reusable abstractions, platform capabilities, and engineering patterns that amplify team leverage.
- Providing technical leadership through design reviews, implementation guidance, incident reviews, and mentorship for senior and mid-level engineers.
- Influencing roadmap and prioritization discussions by connecting platform investments to reliability, scalability, and long-term engineering efficiency.
Technology stack includes Python, SQL, Scala, Java, Snowflake, Databricks, S3, Postgres, Redis, AWS, Kafka, Pulsar, Spark, Terraform, Docker, and Kubernetes.
Requirements:
- 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.
- 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.
Bonus qualifications include experience with highly available, fault-tolerant data platforms; disaster recovery, replay, idempotency, and backfill patterns; multi-tenant or enterprise-scale data delivery; platform standardization and reusable framework design; leading architecture across multiple teams; and operational excellence for business-critical workflows.
