Senior Software Engineer

Blackpoint Cyber

16d•CA$131,000 - CA$164,000•Remote

About The Position

Blackpoint Cyber is the leading provider of world-class cybersecurity threat hunting, detection and remediation technology. Founded by former National Security Agency (NSA) cyber operations experts who applied their learnings to bring national security-grade technology solutions to commercial customers around the world, Blackpoint Cyber is in hyper-growth mode, fueled by a recent $190m series C round. We are seeking a Sr. Backend / Data Software Engineer who will help solve complex technical and operational problems in the cybersecurity industry. Candidates will directly support Blackpoint's cybersecurity mission, helping to stop cyber-attacks and protecting Blackpoint's rapidly growing customer base. Summary: The Product Engineering team is seeking an experienced Backend / Data Software Engineer who will thrive in a fast-paced, remote environment. In this role, you will own the backend services and data platform that power our cybersecurity products — from high-volume ingestion of security telemetry to the pipelines, storage, and APIs that turn that data into protection for our customers. This position is ideal for a strong, versatile software engineer: someone equally comfortable building and operating data infrastructure and stepping into general backend engineering work wherever the team needs it most.

Requirements

5+ years of industry experience in backend and data engineering, with demonstrated expertise delivering scalable, high-performance systems in fast-paced, enterprise-level environments.
Strong programming skills in at least two of TypeScript/Node.js, Python, and Go, with experience building and maintaining microservices architectures on Kubernetes.
Hands-on experience building and operating production ETL/ELT and streaming data pipelines, including workflow orchestration (e.g., Airflow, Dagster) and stream processing (e.g., Kafka Streams, Flink, or Spark).
Experience with data warehouse/lakehouse technologies (e.g., Snowflake, BigQuery, Redshift, or S3 with Parquet/Iceberg), including analytical data modeling for large datasets.
Strong expertise in database management, with experience in schema design, performance tuning, and indexing for large-scale distributed databases (e.g., PostgreSQL, MongoDB, or Cassandra).
Experience with message- and event-driven architectures using tools like Kafka, RabbitMQ, or other event-streaming systems to build highly scalable, decoupled services.
Expertise in designing and consuming APIs (REST, GraphQL, or gRPC), with a focus on creating secure, scalable, and high-performance services.
Commitment to data quality and observability — pipeline testing, monitoring, alerting, and lineage — with hands-on experience using Prometheus and Grafana to maintain system performance and health.
Experience with cloud-native technologies on AWS (e.g., Lambda, S3, EKS/ECS, Kinesis, SQS) and designing for cost-efficient operation at scale.
Hands-on experience with CI/CD pipelines (Jenkins, GitLab, or similar), automating testing, deployment, and scaling of services.
A deep understanding of security best practices, such as securing APIs, handling sensitive data, and managing authentication/authorization (OAuth, JWT).
Strong desire to work in a fast-paced, start-up environment with multiple releases a day, and experience working in an Agile development environment — leading sprint planning, breaking down large initiatives, and aligning team efforts with business objectives.
Exceptional analytical and problem-solving abilities, with a proven track record of diagnosing complex issues, devising effective solutions, and implementing innovative approaches to overcome technical challenges.
Demonstrated ability to leverage AI-assisted development tools to accelerate delivery while maintaining rigorous code quality, security review, and architectural integrity.

Nice To Haves

Experience working with security data at scale — EDR/endpoint telemetry, network logs, SIEM data, or similar high-volume event streams.
Experience with Elasticsearch or similar search engines for building robust indexing and search capabilities in backend services.
Expertise in security best practices, including penetration testing and vulnerability assessment, to safeguard applications against potential threats.
Experience evaluating and integrating LLM-based capabilities or AI features into production SaaS products, including considerations around prompt security, output validation, and responsible AI disclosure.

Responsibilities

Deliver on business-critical outcomes through your contributions to our cybersecurity platform, owning backend and data services end-to-end — from design and architecture through production operation.
Design, build, and operate scalable data pipelines and streaming ingestion for high-volume security telemetry, ensuring reliability, low latency, and cost efficiency.
Contribute to data modeling and warehouse/lakehouse architecture decisions that serve detection, analytics, and product features.
Deliver projects across our stack — TypeScript/Node.js, Python, and Go services hosted on Kubernetes — flexing between data engineering and general backend work as priorities shift.
Leverage your deep understanding of design and architecture, along with best practices in design patterns. Your experience in optimizing for performance, scale, and resiliency will help deliver outcomes that withstand the test of time.
Excel as a team player by recognizing the importance of diverse perspectives and managing expectations and needs across numerous cross-functional groups. This includes mentoring other engineers on day-to-day technical and design questions.
Communicate effectively with leadership to define technical solutions, aligning them with high-level business objectives and providing clear progress updates.
AI-Driven Development Acceleration – Identify, evaluate, and implement AI-powered tools and practices that enhance development, testing, delivery, and observability. Champion the responsible use of AI to automate routine tasks, improve engineering workflows, and boost system reliability and team productivity.