Backend Engineer - Event Processing & Intellige...

Secomea; København; Hybrid; DKK 650,000 - 900,000; Full time

Job summary

Secomea in Denmark is seeking a Backend Engineer to design event-driven pipelines and backend services on top of our audit platform. You will process audit events, enrich with context, and provide APIs for SIEM and customer tooling.

The role blends backend development, data engineering, and production operations using C#/.NET and Python on Azure, with a focus on reliability and scalable design. You will collaborate with the Data & AI team to deliver production-ready solutions, maintain CI/CD,

Qualifications

• Solid experience with C#/.NET backend development.
• Proficiency in Python for data processing and pipeline work.
• Strong experience with event-driven and asynchronous systems— queues, event buses, or message brokers.
• Experience designing ETL or data pipelines in a production environment.
• Experience building REST APIs (and streaming/event-driven interfaces).
• Experience with cloud-based data platforms (Azure Databricks) and Python/C# production environments.
• Understanding relational/distributed data stores and schema evolution.
• Experience building multi-tenant systems with proper data isolation.
• CI/CD with GitHub Actions.
• Strong focus on code quality, testing, and maintainability.

Responsibilities

• Build event-driven pipelines in C# and Python that ingest audit events and produce queryable records.
• Design robust processing with retries, out-of-order delivery, poison messages, backpressure, at-least-once semantics.
• Decide how pipeline output is stored and versioned with an append-only audit log.
• Turn notebook analytics into unattended services with scheduling, retries, throughput, cost, monitoring.
• Develop backends to separate insights from noise: aggregation across sessions/users/devices, baselines, risk scoring.
• Enrich events with severity, reasoning, and product links.
• Design APIs to expose outputs to UI, SIEM, and downstream services.
• Build integrations to deliver notifications and structured evidence into customer tooling.
• Create clean data contracts and event formats for compliance and analytics.
• Ensure APIs are robust, performant, scalable to growing event volumes.
• Operate services: monitoring, alerting, tracing, incident response across Azure/Databricks.
• Build observability to detect output quality issues.
• Maintain CI/CD pipelines, automated testing, and safe deployments.

Skills

C#/.NET

Python

Event-driven systems

APIs REST

Azure Databricks

GitHub Actions

Multi-tenant

Data stores

Tools

Azure Databricks

Job description

Location:On-site with possibility to work remote

Team:Data & AI

Seniority:Mid-Level to Senior

About the Platform

Our team is building the platform that captures, stores, and exposes the complete history of mission-critical OT (Operational Technology) asset access. Through event ingestion, audit logging, activity tracking, and data services, we provide the foundation for compliance and security reporting as well as AI-driven insights. Reliability, traceability, and data integrity are at the core of what we do.

About the Role

We are looking for a Backend Engineer with a strong hybrid skill set across C#/.NET and Python to help us build the services that turn our audit and activity data into insight. Your main mission is to design and build the event-driven pipelines and backend services that sit on top of the audit platform: processing events as they arrive, enriching them with context, detecting meaningful patterns, and delivering the result to the people and systems that need to act on it.

You will work closely with colleagues in the Data & AI team to take analytical work from proof of concept into production. That means the plumbing that makes intelligence useful: pipelines triggered by platform events and completing inside a tight SLA, structured outputs stored in a schema that can evolve, APIs that expose those outputs cleanly, and delivery into customer tooling such as SIEM and collaboration platforms. Our data workflows run on Azure Databricks and our services run on Azure, and you will own significant parts of how these two worlds fit together.

This is a hands-on engineering role at the intersection of event-driven backend development, data engineering, and production operations. You will influence architecture across processing pipelines, detection logic, and integration interfaces.

We are looking for a self-driven and curious engineer who thrives in a collaborative environment. You value a healthy feedback culture, are open and approachable, and enjoy working closely with colleagues, ideally from the office, to solve problems together. You are focused, eager to learn, and motivated to continuously improve both our processes and your own work. You are comfortable navigating external constraints and can design robust solutions within those boundaries. You maintain a practical balance in documenting your work: thorough enough to ensure clarity and knowledge-sharing, yet efficient enough to avoid unnecessary overhead.

Beyond all that, this role is a fantastic chance to grow professionally, personally, and as part of a team that genuinely enjoys building great things together and having fun along the way.

What You Will Do

Event Processing & Pipelines

• Build event-driven pipelines in C# and Python that pick up audit events as they land, process them inside a time budget, and turn them into records the product and customer tooling can query

• Design processing that is correct under real-world conditions: retries, out-of-order delivery, poison messages, backpressure, and at-least-once semantics

• Decide how pipeline output is stored and versioned. Our audit data is append-only, so there is no going back and rewriting it. A schema decision here is closer to permanent than in most systems

• Take analytical work that has been proven in a notebook and make it a service that runs unattended: scheduling, retries, throughput, cost, and the monitoring that catches it when it stops being accurate

Detection & Enrichment Services

• Build the backend that separates the handful of events worth investigating from the thousands that are routine: aggregation across sessions, users and devices, time windows, behavioral baselines, and risk scoring

• Keep the signal-to-noise ratio high enough that customers leave the feature switched on: deduplication, suppression, thresholding, and confidence scores that are honest about uncertainty

• Design a rules layer that customers can configure, without every new detection requiring a release

• Enrich raw events with the context that makes them actionable: severity, reasoning, and links back into the product

APIs & Integration

• Design and build the C# APIs that carry this output to the three places it gets consumed: our own product UI, customer SIEM platforms, and downstream services

• Build integrations that deliver notifications and structured evidence into customer tooling (SIEM platforms, collaboration tools, downstream services)

• Design clean data contracts and event formats suitable for compliance, security, and analytics consumers

• Ensure APIs are robust, performant, and scale with growing event volumes

Running What We Build

• Operate the services you build: monitoring, alerting, tracing, and incident response across Azure, Databricks, and our messaging layer

• Build the observability that tells us when output quality degrades, not just when a service is down

• Maintain CI/CD pipelines, automated testing, and safe deployment practices for the services you own

What You Bring

Required Qualifications

• Solid experience withC#/.NETbackend development

• Working proficiency inPythonfor data processing and pipeline work — you do not need to have been a Python-first engineer, but you should be productive in it

• Strong experience withevent-driven and asynchronous systems— queues, event buses, or message brokers — and a clear understanding of their failure modes

• Experience designing and implementingETL or data pipelinesin a production environment

• Experiencedesigning and building APIs(REST, and ideally streaming or event-driven interfaces)

• Experience withcloud-based data platforms(preferably Azure Databricks) and with working across a Python data stack and a C#/.NET production environment

• Solid understanding of relational and/or distributed data stores, including schema design and evolution

• Experience buildingmulti-tenant systemswhere data isolation is a correctness requirement — authorization enforced in the data access path rather than bolted on at the edge

• Experience with CI/CD tools (GitHub Actions)

• Strong sense of code quality, testing, and maintainability

Nice to Have

Experience with any of the following is a plus:

Platform and engineering breadth

• Queuing technology (e.g., RabbitMQ)

• Data streaming architectures (Kafka, Event Hubs, change data capture)

• Kubernetes and container-based deployments (Docker, AKS)

• Infrastructure as Code (e.g., Bicep, Terraform)

• Workflow orchestration tools (Databricks Workflows, Airflow, Azure Data Factory)

• Azure services (Functions, Container Apps, Data Lake, API Management, Monitor / Log Analytics)

• Observability tooling (e.g., Prometheus, Grafana, OpenTelemetry)

• Distributed systems failure modes and resilience patterns

• Append-only / immutable data stores and time-series databases

Domain

• Familiarity with SOC tooling and concepts (SIEM integration, log forwarding, security event formats such as CEF/LEEF)

• Rules engines or customer-configurable detection logic

• Exposure to OT,ICS, or other mission-critical / regulated environments

• Familiarity with compliance frameworks such as NIS2, ISO 27001, or IEC 62443

Machine learning and data science

• Working alongside data scientists or ML engineers to productionize models — packaging, serving, and versioning

• MLOps practices: experiment tracking, model registries, evaluation pipelines, confidence calibration, and drift detection (MLflow or similar)

• Databricks and PySpark, Delta Lake, or other lakehouse technologies

• Practical exposure to anomaly detection, behavioral baselining, or statistical/ML approaches to event data

• Applied use of LLMs in production systems — structured output, evaluation, and cost control

Why Join Us

You will be joining an ambitious and fast-growing team that is shaping how data and AI power our products. We move quickly, take ownership of what we build, and care about doing things well. This is a place where your work has visible impact, where your ideas are heard, and where you can grow alongside colleagues who genuinely enjoy solving hard problems together. If you want to be part of building something meaningful with a team that values curiosity, collaboration, and craftsmanship, you will feel at home here.