Principal, Systems Engineer (R-18971)

Strong experience working with large-scale IP, domain, internet infrastructure, cyber intelligence, adtech, threat intelligence, fraud, or similar datasets.

Hands-on experience resolving technical internet signals to real-world entities, companies, organizations, or business locations.

Strong SQL skills for large-scale data analysis, joins, filtering, deduplication, quality checks, and exception analysis.

Strong Python skills for data processing, automation, file handling, matching logic, validation workflows, and repeatable pipelines.

Working knowledge of IP address ranges, ASNs, BGP/routing behavior, DNS, reverse DNS, domains, hosting infrastructure, cloud providers, CDNs, ISPs, and IP geolocation.

Ability to distinguish business-owned infrastructure from infrastructure that is residential, consumer, mobile, proxy/VPN, cloud-hosted, CDN, parked, stale, or otherwise weakly attributable.

Experience working with messy, high-volume data where the answer is probabilistic rather than perfectly deterministic.

Strong judgment in entity resolution: knowing when to map, when not to map, when to lower confidence, and when to escalate for review.

Ability to document rules, assumptions, confidence logic, QA findings, and repeatable workflows clearly.

Operate comfortably with very large-scale IP and internet intelligence datasets, including files covering billions of IP addresses.

Understand how to assess whether internet infrastructure is meaningfully attributable to a business, organization, or commercial entity.

Apply strong technical judgment to distinguish business-relevant infrastructure from consumer, residential, mobile, cloud-hosted, CDN, proxy/VPN, parked, stale, or otherwise weakly attributable signals.

Work across IP-level, domain-level, routing, hosting, geolocation, and other internet-layer signals to support high-quality business entity attribution.

Build and maintain repeatable data-processing workflows that support ingestion, normalization, analysis, validation, quality review, and delivery of large-scale attribution outputs.

Use SQL and Python to analyze large datasets, identify signal patterns, automate repeatable steps, investigate exceptions, and support scalable production processes.

Develop practical rules, heuristics, confidence logic, and review frameworks for working with noisy, incomplete, or conflicting attribution signals.

Evaluate the quality, coverage, freshness, explainability, and commercial usefulness of third-party IP intelligence and internet-signal data sources.

Partner effectively with product, data science, engineering, customer-facing, and data operations teams to translate attribution work into reliable business outputs.

Produce clear documentation of assumptions, quality standards, confidence levels, known limitations, and operational procedures.

Support customer-facing and downstream product use cases by ensuring outputs are reliable, explainable, and fit for purpose.

Provide technical guidance to analysts or junior engineers involved in validation, exception review, quality control, or recurring production workflows.