6 months ago
Amsterdam, NetherlandsStaff+
Responsibilities
- Design, build, and operate large-scale data pipelines for identity-linked data ingestion, transformation, and distribution.
- Architect ETL/ELT workflows using distributed computing frameworks on cloud infrastructure.
- Build API-first, platform-grade services that expose Identity capabilities as reusable self-service building blocks.
- Define observability, data quality validation, monitoring, SLAs, SLOs, and operational metrics for pipelines and services.
- Drive performance optimization, cloud cost efficiency, reliability, scalability, and reusable platform patterns.
- Own the technical evolution of probabilistic and deterministic matching, device graph construction, and audience segmentation systems.
- Architect secure data collaboration, clean room workflows, and privacy-preserving computation while applying privacy-by-design principles.
- Set technical direction, produce architecture and design documentation, and drive alignment across engineering, product, commercial, data science, and partner stakeholders.
- Lead code and design reviews, improve testing and deployment practices, and establish shared engineering standards.
- Mentor engineers and partner with the Engineering Manager on roadmap shaping, technical planning, and systemic risk resolution.
- Own end-to-end reliability, incident response, post-mortem follow-through, and continuous system health improvements.
Requirements
- 12+ years of professional software engineering experience with deep expertise in data engineering, backend systems, or distributed data infrastructure.
- Proven ability to own technical architecture end-to-end in a complex production data environment.
- Strong command of Python and SQL, with comfort using JavaScript in full-stack or API contexts.
- Extensive production experience with distributed processing frameworks such as Spark or Databricks on large-scale datasets.
- Experience with AWS and/or GCP and their core data services.
- Experience with workflow orchestration tools such as Apache Airflow, dbt, or Prefect.
- Strong familiarity with data warehousing and lakehouse architectures, including Snowflake.
- Experience building platform-grade, API-first systems for internal or external downstream consumers.
- Deep understanding of GDPR and CCPA and practical experience building privacy-compliant systems handling PII at scale.
- Familiarity with secure data collaboration, data clean rooms, or privacy-preserving computation.
- Exceptional communication, technical writing, cross-functional collaboration, mentoring, and alignment skills.
- Preferred: experience in ad tech, identity resolution, data licensing, digital media, device graphs, audience segmentation, or programmatic data flows.
- Preferred: experience with Kafka, Flink, or Spark Streaming.
- Preferred: experience incorporating AI and machine learning capabilities into production data workflows.
- Preferred: track record of cross-team technical leadership through architecture documentation, engineering standards, or mentorship programs.
Benefits
- Health insurance, wellness offerings, life and disability insurance, a retirement savings plan, paid holidays, paid time off, and other employee benefits.
- Additional rewards may include bonuses, short-term incentives, and long-term incentives.
Tech Stack
Apache AirflowApache FlinkApache KafkaApache SparkAWSDatabricksdbtGoogle Cloud PlatformJavaScriptPythonSnowflakeSQL
Categories
BackendData Engineering
