Contract Role: Sr Data Engineer at Bellevue, WA or Kansas City, KS (Onsite – Hybrid Model)

Sr Data Engineer (Open Source Platform) Bellevue, WA or Kansas City, KS (Onsite – Hybrid Model) Long Term Contract Job Description: • 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.