Data Engineer – Cybersecurity

Overview

We are seeking Senior Data Engineers to lead efforts in orchestrating and transforming complex security telemetry data flows. These individuals will be responsible for high-level architecture, governance, and ensuring secure and reliable movement of data between systems, particularly for legacy and non-standard log sources. There are 100+ data sources including existing and new that are specific to Cyber Security workloads that are in-scope.

These tasks will be performed on one or more data ingestion pipelines (Cribl, Vector, NiFi).

• Lead the architecture, design, and implementation of scalable, modular, and reusable data flow pipelines using Cribl, Apache NiFi, Vector, and other open-source platforms, ensuring consistent ingestion strategies across a complex, multi-source telemetry environment.
• Develop platform-agnostic ingestion frameworks and template-driven architectures to enable reusable ingestion patterns, supporting a variety of input types (e.g., syslog, Kafka, HTTP, Event Hubs, Blob Storage) and output destinations (e.g., Snowflake, Splunk, ADX, Log Analytics, Anvilogic).
• Spearhead the creation and adoption of a schema normalization strategy, leveraging the Open Cybersecurity Schema Framework (OCSF), including field mapping, transformation templates, and schema validation logic—designed to be portable across ingestion platforms.
• Design and implement custom data transformations and enrichments using scripting languages such as Groovy, Python, or JavaScript, while enforcing robust governance and security controls (SSL/TLS, client authentication, input validation, logging).
• Ensure full end-to-end traceability and lineage of data across the ingestion, transformation, and storage lifecycle, including metadata tagging, correlation IDs, and change tracking for forensic and audit readiness.
• Collaborate with observability and platform teams to integrate pipeline-level health monitoring, transformation failure logging, and anomaly detection mechanisms.
• Oversee and validate data integration efforts, ensuring high-fidelity delivery into downstream analytics platforms and data stores, with minimal data loss, duplication, or transformation drift.
• Lead technical working sessions to evaluate and recommend best-fit technologies, tools, and practices for managing structured and unstructured security telemetry data at scale.
• Implement data transformation logic including filtering, enrichment, dynamic routing, and format conversions (e.g., JSON CSV, XML, Logfmt) to prepare data for downstream analytics platforms. (100 plus sources of data)
• Contribute to and maintain a centralized documentation repository, including ingestion patterns, transformation libraries, naming standards, schema definitions, data governance procedures, and platform-specific integration details.
• Coordinate with security, analytics, and platform teams to understand use cases and ensure pipeline logic supports threat detection, compliance, and data analytics requirements.