Banank is seeking an experienced Lead Data Engineer to define the architecture and lead the development of our next-generation real-time data platform, powering backend services, business intelligence, operational decision-making, and AI-driven analytics across our digital banking ecosystem.
This role goes beyond building data pipelines you will own the evolution of Banank's enterprise data platform, defining architectural principles, engineering standards, and modern data engineering practices for real-time processing, data governance, and scalable data products.
You will be responsible for the company's enterprise data architecture, ensuring data is consistent, discoverable, reusable, and readily available for operational services, business intelligence, regulatory reporting, and AI-driven applications.
Working closely with Platform Engineering, Backend Engineering, Product, Security, and Business teams, you will design streaming-first data systems supporting operational workloads such as transaction processing, fraud detection, customer onboarding, personalization, technical analytics, and business intelligence while ensuring high availability, consistency, low latency, and operational resilience.
You will also help establish modern data engineering practices, including Customer 360 data models, enterprise data governance, semantic data models, and natural language analytics capabilities that enable business users to interact with enterprise data using AI-powered tools.
We understand that it may be challenging to find a candidate with experience across every technology listed below. We are not looking for a perfect match we're looking for someone who is curious, adaptable, and excited about solving complex engineering problems while learning new technologies.
If you have strong fundamentals in distributed data systems and enjoy building modern real-time platforms, we'd love to hear from you.
Key Responsibilities
Enterprise Data Architecture
- Own the architecture and long-term evolution of Banank's enterprise data platform.
- Define scalable architectural principles for operational, analytical, and real-time data systems.
- Design reusable enterprise data models and data products supporting backend services, business intelligence, regulatory reporting, and AI applications.
- Ensure enterprise data remains consistent, governed, discoverable, and reusable across the organization.
Real-Time Data Processing
- Design and implement low-latency streaming pipelines supporting operational banking workloads.
- Build pipelines as code, ensuring fully automated, version-controlled, and reproducible infrastructure.
- Enable backend services to consume real-time streams.
Streaming & Analytical Data Platform
- Implement and operate streaming-first data platforms and distributed analytical query engines.
- Design modern data lakehouse architectures supporting both streaming and batch workloads.
- Build scalable serving layers capable of supporting operational APIs, business intelligence, and advanced analytics.
Customer 360 & Data Products
- Design and maintain C360 models providing a unified customer view across banking services.
- Develop reusable enterprise data products consumed by engineering teams, business stakeholders, and AI-driven services.
- Design semantic data models enabling self-service analytics and natural language access to enterprise data.
AI & Natural Language Analytics
- Design data models supporting semantic querying and natural language interaction with enterprise data.
- Collaborate with AI teams to enable business users to explore operational and analytical data using LLMs.
- Ensure the data platform is AI-ready and capable of supporting future intelligent automation and decision-support solutions.
Data Quality & Governance
- Establish enterprise-wide standards for data quality, monitoring, lineage, validation, and governance.
- Ensure correctness, consistency, idempotency, fault tolerance, and observability throughout real-time data pipelines.
Performance & Reliability
- Optimize latency, throughput, scalability, and resource utilization.
- Design resilient architectures capable of supporting high-volume banking workloads.
Technical Leadership
- Mentor Data Engineers and promote engineering excellence.
- Define engineering standards, architectural guidelines, and best practices.
- Evaluate emerging technologies through PoCs and recommend adoption where appropriate.
Core Requirements
- 6+ years of experience in Data Engineering, with a strong focus on streaming and real-time platforms.
- Proven experience designing enterprise data platforms serving operational backend systems not only analytical workloads.
- Strong understanding of modern data architecture, including real-time processing, lakehouse concepts, and distributed data systems.
- Hands-on experience with streaming data platforms and distributed analytical query engines.
- Strong understanding of event-driven architectures and streaming consistency models.
- Experience designing high-throughput, low-latency distributed systems.
- Experience implementing pipelines as code, automated deployment, and version-controlled data infrastructure.
- Experience building Customer 360 or similar enterprise customer data models is highly desirable.
- Experience building cloud-native enterprise data platforms.
- Strong programming skills (Java, Python, or similar).
- Strong written and spoken English.
- Must have the legal right to work in the EU. Candidates from CIS countries may be considered under a contractor engagement model.
- Ability to work effectively in a fully remote environment.
Join the Banank family!
We're growing fast, building exciting things, and creating the next generation of digital banking!
