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Iterable is a leading AI-powered customer engagement platform trusted by 1,200+ brands globally (including Redfin, SeatGeek, Priceline, Calm, Box) to activate customer data, design cross-channel interactions, and optimize engagement at scale.
As a Data Engineer II, you will own moderate-scope projects building reliable data pipelines and platform capabilities that power customer-facing data movement, analytics products, and machine learning foundations. You'll work with regular guidance on newer or more ambiguous problems while collaborating across engineering, product, infrastructure, and data science teams.
Key responsibilities include:
- Build and operate next-generation data ingestion and activation platform components: source connectors, staging/transformation workflows, diffing and incremental sync logic, and Iterable bulk API integrations
- Improve pipeline reliability, observability, and recovery through metrics, dashboards, alerting, retries, and durable workflow execution
- Develop and maintain Snowflake-based data pipelines and data-sharing workflows for analytics products and customer-facing data delivery
- Contribute to schema evolution, data quality checks, and operational processes that reduce discrepancies
- Partner with data science and ML engineers to build infrastructure for feature generation, feature serving, model inputs, and experimentation
- Make thoughtful tradeoffs across batch/streaming patterns, data modeling, performance, and maintainability
- Collaborate cross-functionally to define interfaces, debug issues, and deliver end-to-end solutions
- Proactively anticipate roadblocks and navigate business unit processes
Required qualifications: 3+ years in data engineering, software engineering, or adjacent platform/infrastructure roles; production data pipeline and ETL/ELT systems experience at scale; strong Python and SQL proficiency plus familiarity with Scala or Java; experience with Snowflake, S3, Databricks, Postgres, Redis, or similar; familiarity with orchestration/workflow concepts and batch/streaming design; understanding of data modeling, schema evolution, incremental processing, testing, and observability; comfort in remote/distributed collaborative environments; willingness to participate in on-call support; fluent English.
