Data Engineer

About Huskeys :

Huskeys is reimagining web application protection for the modern era.

As applications become increasingly dynamic, multi-cloud, and AI-driven, traditional WAFs struggle to keep up.

We built the first Agentic Network Security Control Plane - an AI-powered mitigation layer that works with any existing WAF. Our platform helps security teams unify protection, measure effectiveness, reduce manual overhead, and create context-aware policies that block threats without disrupting business flows.

At Huskeys, we believe context is the new attack surface, and that security should empower the business - not slow it down.

About the Role

At our core, we are a fundamentally data-driven platform. We ingest massive volumes of logs, scans, and telemetry data daily, meaning the core value we deliver to our customers depends entirely on how efficiently we can partition, aggregate, and cross-reference this information.

Up until now, this heavy lifting has been distributed across the engineering team. Today, we are looking for a dedicated Data Engineering owner to step in, take full autonomy, and drive three critical pillars:

• Scale & Performance: As our log volume and aggregation complexity skyrocket, you will design the partitioning and aggregation strategies that truly scale. We are moving away from ad-hoc queries that break under pressure and investing in a resilient, long-term data layer architecture.

• Data Integrity & Trust: The decisions our platform automates are only as good as the data fueling them. You will build and optimize reliable ETL pipelines from a diverse set of sources (cloud providers, external scans), establishing the data consistency guarantees that allow our entire engineering team to build with absolute confidence.

• Laying the Foundation for ML: While we aren't building Machine Learning models just yet, we want to be fully ready when we do. You will ensure our data is structured, pristine, and highly accessible from day one, so that when the time comes, we can integrate ML seamlessly without having to re-plumb our entire infrastructure.

What You'll Own

• Table & schema structure: Design and own our table structures - data models, keys, types, partitioning, and relationships - and the unified models that turn many sources into one coherent, queryable shape. Log analysis & partitioning: Design and own how we store and partition large-scale log data so it stays fast and quarriable as volume grows.

• Complex aggregation & cross-referencing: Build and optimize the complex aggregations and cross-source joins that power our platform's insights and decisions. ETLs from multiple sources: Build and maintain reliable ETL/ELT pipelines from many different sources - cloud providers and outside scans - landing diverse inputs cleanly into our unified tables.

• Data integrity & observability: Ensure data consistency and correctness, and monitor pipeline health, freshness, and quality with alerting - so we meet our SLAs and catch bad data before it reaches the product.

• Data governance: Handle sensitive customer data responsibly - access control, retention, and privacy - in line with our security and compliance commitments.

• Scaling strategy: Own the strategy for scaling our data and aggregations - table design, partitioning, query performance, and cost - ahead of where we are today. Setting the stones for ML: Structure and prepare data (clean, feature-ready tables and pipelines) so we're ready to add machine learning when the business is.

What You Bring

• 8+ years of data engineering (or strongly data-focused backend) experience.
• Expert-level data lakes - designing schemas, modeling data, and writing and optimizing complex aggregation and cross-referencing queries.
• Strong Python for building ETL/ELT pipelines and data tooling.
• Large-scale data experience - has worked with high-volume log/event data, including partitioning and performance optimization (ClickHouse, Athena, or similar columnar/analytical stores).
• ETL/pipeline experience - has built and maintained reliable pipelines ingesting from multiple, diverse sources.
• Data integrity & consistency - solid understanding of data quality, consistency, and reliability across distributed environments.
• Scaling mindset - can design data and aggregation strategies that scale, not just work today.

Why Join Us 

This is an opportunity to join building the ops function from the ground up in a fast moving cybersecurity startup attacking a large legacy market. You will have real ownership, close partnership with all departments and the ability to shape how Huskeys moves fast and grow safely with impact. Growth here isn't a fixed ladder; it's continuous, shaped by your initiative