Data Engineer II
Iterable
| Company | Iterable |
| Category | Engineering |
| Location | Lisboa |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 30 Jul 2026 |
| Last verified | 1 Aug 2026 |
| Source | Employer career page (ashby) |
Description
How you will make an impact:
As a Data Engineer II 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 own moderate-scope projects with regular guidance on newer or more ambiguous problems, while collaborating 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, and operational visibility across the platform.
HOW YOU WILL MAKE A DIFFERENCE:
- Build and operate components of 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.
- 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 that improve freshness, correctness, and scalability for analytics products and customer-facing data delivery.
- Contribute to schema evolution, data quality checks, and operational processes that reduce discrepancies and make data systems easier to support and extend.
- Partner with data science and machine learning engineers to build and harden the data infrastructure behind feature generation, feature serving, model inputs, and experimentation workflows.
- Make thoughtful tradeoffs across batch and streaming patterns, raw-to-curated data modeling, performance, and maintainability as systems scale.
- Collaborate with cross-functional partners across product, frontend, platform, SRE, and customer-facing teams to define interfaces, debug issues, and deliver high-quality solutions end to end.
- Recommend improvements to existing processes and understand resources available to overcome unforeseen issues.
- Learn to proactively anticipate small roadblocks to accomplishing tasks and use knowledge of business unit processes to navigate them successfully.
WE ARE LOOKING FOR PEOPLE WHO HAVE:
- 3+ years of relevant experience in data engineering, software engineering, or adjacent platform and infrastructure roles.
- Experience building 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.
- Familiarity with orchestration and workflow concepts, and with designing batch and/or streaming data systems.
- Understanding of data modeling, schema evolution, incremental processing, testing, and observability for reliable data products.
- 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:
- Programming Languages: Python, SQL, Scala and/or Java
- Data Platforms & Databases: Snowflake, Databricks, S3, Postgres, Redis
- Infrastructure: AWS, Kafka, Pulsar
- Other Relevant Technologies: Spark, Terraform, Docker / Kubernetes, ETL/ELT orchestration
BONUS POINTS
- Experience with Apache Spark, feature stores, or ML platform data workflows.
- Experience with Kafka, Pulsar, Airflow, or similar event-driven and workflow technologies.
- Experience with AWS and infrastructure-as-code tools such as Terraform.
- Experience supporting enterprise-grade data products with strict reliability, freshness, or customer delivery expectations.
- Experience with end-to-end, integration, or performance testing for data-intensive
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