22 days ago
Madrid, SpainStaff+
Responsibilities
- Own the end-to-end architecture, design, and evolution of enterprise data platform capabilities across hybrid cloud and on-premises environments.
- Define technical roadmaps, engineering standards, and reusable patterns for workflow orchestration, scheduling, dependency management, and data processing.
- Lead architectural decisions, proofs of concept, and adoption plans focused on scalability, interoperability, security, operability, and total cost of ownership.
- Establish reliability, observability, and operational standards including monitoring, alerting, failure recovery, capacity management, and service-level objectives.
- Drive infrastructure automation, CI/CD, automated testing, and self-service engineering capabilities.
- Integrate batch and streaming pipelines across Snowflake, Airflow, and Kafka.
- Lead design and code reviews, mentor engineers, and contribute directly to architecture and code.
- Partner with architecture, security, governance, and application teams to meet enterprise standards and regulatory requirements.
Requirements
- 12+ years of experience in data or platform engineering, including designing and operating enterprise-scale data platforms.
- Deep hands-on expertise with Snowflake across architecture, ingestion, transformation, workload management, performance, cost optimization, secure data sharing, and governance.
- Strong production experience with Apache Airflow, including reusable DAG patterns, dependency management, scheduling, testing, monitoring, and failure recovery.
- Strong expertise in Apache Kafka and event-driven architectures, including topic design, producers and consumers, schema management, Kafka Connect, delivery semantics, and replay strategies.
- Proven ability to integrate batch and streaming pipelines across Snowflake, Airflow, and Kafka.
- Advanced Python and SQL skills; Java experience for Kafka-based services and connectors is beneficial.
- Strong experience with Terraform, Git, CI/CD, and automated testing; OpenShift or Kubernetes experience is desirable.
- Understanding of reliability, observability, data quality, security, access management, and compliance in regulated environments.
- Proven technical leadership in setting engineering direction, reviewing designs and code, mentoring engineers, and communicating with technical and business stakeholders.
- Fluent written and spoken English, strong problem-solving and strategic-thinking skills, and the ability to use AI tools responsibly and ethically.
- A relevant degree is advantageous.
Benefits
- Hybrid working with the balance between office and home working determined by the business group and discussed during the application process.
- Flexible benefits plan with options covering healthcare, company perks, and retirement planning.
- Inclusive workplace culture focused on diversity, equity, employee wellbeing, and collaboration.
- Interview adjustments and assistance are available for disabilities or long-term health conditions.
Tech Stack
Categories
Data Engineering
