About DWS
Today, markets face a whole new set of pressures – but also a whole lot of opportunity too. Opportunity to innovate differently. Opportunity to invest responsibly. And opportunity to make change. Join us at DWS, and you can be part of an industry-leading firm with a global presence. You can lead ambitious opportunities and shape the future of investing. You can support our clients, local communities, and the environment. We’re looking for creative thinkers and innovators to join us as the world continues to transform. As whole markets change, one thing remains clear; our people always work together to capture the opportunities of tomorrow. As investors on behalf of our clients, it is our role to find investment solutions. Ensuring the best possible foundation for our clients’ financial future. And in return, we’ll give you the support and platform to develop new skills, make an impact and work alongside some of the industry’s greatest thought leaders. This is your chance to achieve your goals and lead an extraordinary career. This is your chance to invest in your future. Read more about DWS and who we are here.
Team / division overview
DWS’s data infrastructure team provides the secure, scalable and trustworthy foundation for storing, processing and distributing critical enterprise data. As Lead Engineer - Data Platform, you drive the design, built, and continuous improvement of our hybrid data platform. You’ll lead a small but high-impact team, setting technical direction, mentoring team members and contributing directly to architecture and code.
Your responsibilities
As our Lead Engineer – Data Platform, you will:
- Own the end-to-end architecture, design and evolution of enterprise data platform capabilities across hybrid cloud and on-premises environments
- Define the technical roadmap, engineering standards and reusable patterns for workflow orchestration, scheduling, dependency management and data processing
- Lead architectural decisions, proofs of concept and adoption plans based on scalability, interoperability, security, operability and total cost of ownership
- Establish reliability, observability and operational standards for data platform components, including monitoring, alerting, failure recovery, capacity management and service-level objectives
- Drive platform automation through infrastructure as code, CI/CD, automated testing and self-service engineering capabilities
- Partner with architecture, security, governance and application teams to ensure platform solutions meet enterprise standards and regulatory requirements
- Remain hands-on in architecture and code, lead design and code reviews, mentor engineers and foster a culture of engineering excellence and continuous improvement
At DWS we’re serious about diversity, equity and creating an inclusive culture where colleagues can be themselves and it’s important to us that you enjoy coming to work - feeling healthy, happy and rewarded. At DWS, you’ll have access to a range of benefits which you can choose from to create a personalised plan unique to your lifestyle. Whether you’re interested in healthcare, company perks, or are thinking about your retirement plan, there’s something for everyone. DWS’ Hybrid Working model is designed to find the right balance between in-person collaboration and engagement in the office, which is core to our working culture, and working from home.
We are looking for:
- 12+ years of experience in data or platform engineering, including designing and operating enterprise-scale data platforms; a relevant degree is advantageous
- Deep hands-on Snowflake expertise across architecture, ingestion, transformation, workload management, performance, cost optimisation, 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
- Strong understanding of reliability, observability, data quality, security, access management and compliance in regulated environments
- Proven technical leadership, including setting engineering direction, reviewing designs and code, mentoring engineers and communicating with technical and business stakeholders
- Strong problem-solving and strategic-thinking skills, with fluent written and spoken English
- Proven ability to leverage AI tools to enhance productivity, optimise workflows to solve business problems, while applying critical judgment to ensure responsible and ethical use of data and AI outputs
