Senior Data Engineer, Product Data Systems

BforeAI is an innovative and rapidly expanding scale-up dedicated to deterring cybercrime through cutting-edge predictive and preemptive technologies. We've harnessed the power of prescriptive AI to revolutionize the way we tackle cyber threats, stopping attacks before they launch.

Named by Gartner® in 41 reports over the last 3 years (14 in 2025 alone!) and recognized as a Tech Innovator for Preemptive Cybersecurity in the 2025 Emerging Tech report, BforeAI is the industry’s fastest, most accurate solution for automated protection against online fraud.

Join us in the fight for a safer cyberspace!

🚀 Why it’s great to work here

We are a location-independent company with a fully distributed team. We believe diversity of experience, background, and thinking leads to stronger teams and a better product. We value autonomy, intellectual honesty, practical decision-making, and engineers who take ownership of outcomes rather than waiting for perfectly defined tasks.

We offer an intellectually stimulating company environment and you’ll be working with a bright, dedicated team from across the globe. Additionally, we offer flexible time off, sick days, all public holidays, and stock options.

✨ What’s cool about this job

We are building the data and intelligence foundation required to turn large, diverse sources of threat data into reliable, explainable, and actionable protection for our customers. You will work with a dedicated global team on technically difficult problems involving cybersecurity, distributed systems, data engineering, machine learning, and SaaS product development.

We are hiring a Senior Data Engineer to build the production services and pipelines that acquire, normalize, enrich, relate, score, and deliver threat intelligence through BforeAI’s customer-facing SaaS product.

📣 What you’ll be doing

This is a hands-on product-engineering role. You will design systems and write production software for event-driven data processing and we need an engineer who understands when a managed service is appropriate, when custom code is necessary, and how to deliver either choice as a reliable product capability.

Build product data services

• Design, implement, test, deploy, and operate production services for ingestion, normalization, enrichment, identity resolution, scoring, and intelligence delivery.

• Build maintainable pipeline workers, event consumers, APIs, scheduled processes, and supporting libraries rather than relying exclusively on notebooks, visual workflows, or vendor configuration.

• Own the complete software lifecycle, including architecture, implementation, testing, deployment, monitoring, incident response, and improvement.

• Establish reusable engineering patterns that other members of the team can follow.

Engineer reliable event-driven pipelines

• Develop asynchronous workflows with explicit data, event, and work contracts.

• Design for duplicate delivery, ordering constraints, idempotency, retries, timeouts, partial failure, dead-letter handling, backpressure, and recovery.

• Make pipeline state and failures observable, with reconciliation that identifies missing, delayed, duplicated, or inconsistent processing.

• Support safe replay and reprocessing without silently changing the meaning of historical results.

Protect data integrity and tenant boundaries

• Preserve source evidence, provenance, lineage, processing context, and applicable versions from the first write so customer-visible results remain explainable.

• Define validation and quality controls at service boundaries and support safe evolution of schemas, contracts, and processing logic.

• Preserve tenant isolation while safely combining shared intelligence with customer-private evidence, configuration, and conclusions.

• Apply authorization, retention, deletion, audit, GDPR, and SOC 2 requirements throughout data-processing workflows.

Shape the architecture and team

• Evaluate when to use a managed service, open-source component, existing capability, or purpose-built service based on product and operational requirements.

• Contribute to architecture through working software, written proposals, prototypes, and constructive technical review.

• Work with Product, Platform Engineering, Threat Research, Data Science, Security, and customer-facing teams. Platform Engineering includes Data Engineering and SRE responsibilities.

• Translate product requirements into clear technical contracts, communicate tradeoffs, and mentor engineers in modern data and distributed-systems practices.

💥 You’ll be a great fit if

• You have significant hands-on experience building and operating data-intensive software for an externally used SaaS product.

• You are a strong software engineer who specializes in data systems, not solely a user or administrator of data tools.

• You have production development experience with Go, Scala, Rust, Java, or another comparable language used to build reliable backend services.

• You are willing to work primarily in Go for pipeline and product data services. Existing Go experience is strongly preferred, but deep experience with Scala, Rust, or similar languages can provide a strong foundation.

• You are proficient with Python and SQL where their data-processing, analytics, integration, or machine-learning ecosystems provide a practical advantage.

• You understand relational, document, graph, key-value, analytical, and object-storage models and their tradeoffs.

• You understand distributed-systems concerns such as asynchronous processing, delivery semantics, concurrency, backpressure, idempotency, consistency, recovery, and failure isolation.

• You have designed or operated event-driven systems using technologies such as Kafka, Azure Event Hubs, AWS Kinesis, or comparable messaging infrastructure.

• You have experience with containers, cloud infrastructure, automated testing, CI/CD, infrastructure automation, monitoring, and production operations.

• You can reason about provenance, replay, data quality, multi-tenant isolation, shared data, private customer context, and authorization boundaries.

• You evaluate unfamiliar technologies based on engineering principles, communicate clearly, challenge weak assumptions constructively, and own delivery outcomes.

You do not need every item below, but relevant experience may help you have an immediate impact:

• Cybersecurity, threat intelligence, fraud, abuse prevention, or other evidence-intensive domains

• Data provenance, explainability, auditability, or regulated data systems

• Graph-based data processing, Neo4j, Azure Data Explorer, or Azure Data Lake Storage

• Machine-learning feature pipelines, model inputs and outputs, or feedback and learning systems

• Migrating legacy batch or pipeline workloads into service-based, event-driven architectures

• Operating customer-facing data systems under defined reliability and recovery expectations

💻 Our environment

Our Azure-based environment includes Go and Python services, Event Hubs and Kafka-compatible messaging, Azure Data Lake Storage, Azure Data Explorer, PostgreSQL, Neo4j, Redis, Databricks for appropriate analytics and machine-learning workloads, Kubernetes, and Azure-managed compute services.

Experience with every component is not required. We care more about whether you understand the engineering principles underneath these technologies, can become productive in an unfamiliar environment, and can choose the appropriate combination of managed services and custom software.

Don't meet every single requirement? Don't count yourself out just yet.

We recognize that strong candidates may have taken different paths and may not match every technology or qualification listed here. If you are an experienced software or data engineer who has built production data-intensive systems, understands the problems underneath the tools, and wants to help build the data foundation of a cybersecurity product, we en