2 months 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
About DWS Group
DWS Group is a global asset manager that provides active, passive (Xtrackers ETFs), and alternative investment products for institutions, financial intermediaries, and individual investors. It earns management and performance fees across mutual funds and bespoke mandates, managing over €1 trillion in assets. Founded in 1956 and headquartered in Frankfurt, DWS is publicly listed in Germany and remains majority-owned by Deutsche Bank.