TechnoSphere, Inc.
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Data Engineer position in Bellevue, WA, with a contract length of "unknown" and a pay rate of "unknown." Key skills include Cribl, Vector, Python, and Apache NiFi. Industry experience in cybersecurity telemetry is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 7, 2025
🕒 - Duration
Unknown
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Bellevue, WA
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🧠 - Skills detailed
#Storage #Data Governance #Monitoring #Anomaly Detection #Data Transformations #Documentation #Security #JSON (JavaScript Object Notation) #Metadata #Data Integration #XML (eXtensible Markup Language) #Snowflake #Kafka (Apache Kafka) #Logging #Groovy #Splunk #Cybersecurity #JavaScript #Libraries #"ETL (Extract #Transform #Load)" #Normalization #Strategy #NiFi (Apache NiFi) #Data Engineering #Scala #Observability #Python #Scripting #Apache NiFi
Role description
Title: Data Engineer
Location: Bellevue, WA
Job description:
Mandatory Skills: Cribl and Vector
• 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.
Title: Data Engineer
Location: Bellevue, WA
Job description:
Mandatory Skills: Cribl and Vector
• 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.
