Sr. Data Engineer - (Cyber Team)

I have 8 open roles for Data Engineer These roles are 4 days onsite, so please send me local candidates. Non–local candidates are also acceptable, but the candidate should be ready to relocate to one of the locations below – no last-minute surprises. This is a cybersecurity role, so data security is more important. Look for candidates coming from Mobility clients, such as T–Mobile, Verizon, AT&T, etc. Job Description Sr. Data Engineer - (Cyber Team) Duration: 12 Months Rate 55 ON C2C Need 14+ Years of exp. Location: Bellevue HQ or Overland Park – onsite 4 days a week • Location 1 (Bellevue) • Location 2 (Overland Park, Kansas) Overview We are seeking eight 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) Project/Initiative: SIEM Modernization Work Required • 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. Skills: data,engineer,security,analytics,platforms,cyber