About the job

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.Security runs on data. Every decision our platform makes - every threat we catch and every business flow we keep open - depends on our ability to ingest, partition, aggregate, and trust massive volumes of log and scan data. This role owns that foundation.

Why we need you?

Our platform is fundamentally data-driven: we ingest huge volumes of logs, scans, and telemetry, and the value we deliver depends on how well we partition, aggregate, and cross-reference all of it. Today this work is spread thin, and we need a dedicated owner for it.You'll own the scale and performance of our data layer - designing partitioning and aggregation strategies that hold up as log volume and complexity grow, rather than ad-hoc queries that fall over. You'll be the guardian of data integrity and trust, building reliable ETLs from many different sources (cloud providers, outside scans) and the consistency guarantees that let the whole team build with confidence. And you'll set the stones for ML: we're not building models yet, but we want our data structured, clean, and accessible so we can add ML without re-plumbing everything.Without this role, our data work stays fragmented, aggregations get slower and less trustworthy as we scale, and we stay blocked from making the most of the data we already have.

About the position

At Huskeys, our platform ingests and cross-references massive volumes of logs, scans, and telemetry from cloud providers, CDNs/WAFs, and external scanning tools. The depth and reliability of our data layer is what makes our context-aware security decisions possible.We are looking for a Data Engineer who can independently own that data layer end-to-end - from table and schema design through partitioning, complex aggregation, and multi-source ETL - and who thinks beyond "can I write this query?" to "will this scale, is the data correct, and can the team build on it?"

What Success Looks Like After 6 Months

Independent Owner: You independently own log analysis and partitioning, our complex aggregations and cross-referencing, and the strategy for scaling our data layer - making architectural calls without constant oversight.
Source of Truth: Our internal data is the best-in-class source of truth across every source we touch - cloud provider and CDN/WAF scans, log analysis, and external scanning - with strong, shared definitions for how we model, unify, and aggregate data.
Scalable Foundation: Adding new complex aggregations is easy and safe at our scale, with best practices built in, and the team has a data strategy everyone can build on.
Business Enabler: Our data layer runs with high integrity and strong SLAs, scales to onboard more clients and new use cases, and deepens our understanding of customers' environments - keeping the product growing in a scalable direction.

Key Responsibilities

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 queryable 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.

Day-to-day Reality

Types of Tasks: Primarily hands-on data engineering and system design - schema and partitioning design, building pipelines, writing and optimizing complex queries. Some meetings for planning and collaboration, but at least 70-80% is technical work.
Level of Independence: High autonomy with end-to-end ownership. When you take on the data layer, it's yours A to Z - design, implementation, monitoring, and long-term maintenance. You make architectural decisions independently with team input.
Pace: Fast-paced and dynamic. Seed-stage startup where priorities can shift; you balance building new data capabilities with keeping existing pipelines reliable.
Collaboration: A mix of independent work and cross-team collaboration. You'll work closely with backend engineers, product, and, over time, anyone building on top of our data.
Tools/Environment: Python, SQL, ClickHouse (columnar/log analysis), PostgreSQL (NeonDB), Temporal for workflow orchestration, AWS Athena, and modern observability platforms (Datadog/Grafana/ELK). We're flexible on exact tools - equivalent experience counts.

The Ideal candidate

You are a pragmatic builder who balances quality and speed - you know when to ship a workable pipeline and when to invest in robust, scalable data architecture.
You are a systems thinker who designs data models, partitioning, and aggregation strategies that hold up as volume grows.
You have an ownership mindset - you take the data layer A to Z without hand-holding, thinking beyond "can I write this query?" to "will this scale, is the data correct, and can the team build on it?"
You are a collaborative engineer who enables teammates through clean, well-modeled, well-documented datasets and knowledge sharing.
You are a strong communicator who can explain data trade-offs to both engineers and non-technical stakeholders.
You thrive in startup environments with ambiguity and rapid change, and you care deeply about data integrity and correctness - not just getting a number out the door.
You're comfortable making decisions independently while seeking input when needed, and you fit the HUSKEY values: accountable, driven, adaptable, sharp, and positive.

Must-Have Requirements

5+ years of data engineering (or strongly data-focused backend) experience.
Expert-level SQL - 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 - hands-on 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.

Note: We're flexible on specific tools. If a candidate has done equivalent work with comparable technologies, that counts - we don't require our exact stack.

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Responsibilities