Architect, build and maintain high-volume, high-fidelity security data modeling in Snowflake system for detection engineering and threat hunting use cases
Lead the ingestion, normalization, enrichment, and correlation of raw security telemetry, including logs, events, metrics, alerts, config, scanning and other data types
Define and enforce data standards, schemas, and normalization frameworks across security data sources.
Leverage SQL, Python, PowerBI and other interfaces in Snowflake to design the right solution for data analytics, including reporting and other metrics.
Partner with Security Operations, Threat detection, GRC and other engineering teams to translate security requirements into scalable data solutions.
Serve as a technical authority and mentor, influencing data engineering, security analytics and platform strategy.
Drive continuous improvement in data reliability, performance, and cost efficiency.
Manage Data Governance with various reporting and quality checks.
Research and look for opportunities to adopt the best practices and industry standards
Identify opportunities to enhance the current baseline processes and configuration
Produce engineering, integration and process related documentation.
Manage vendor relationships to drive roadmap, solution design, implementation and troubleshooting
Able to conduct the POC of new features to develop new solutions
Requirements
At least 10+ years of experience in database engineering with significant focus on security data (logs, scanning, configuration, vulnerability, etc.), analytics, detection and platform engineering
8+ years of hands-on experience with various databases including Snowflake, relational (SQL, PostgreSQL, etc.), no-SQL (MongoDB, DynamoDB, etc.)
At least 2+ years of experience with data warehouses and data lakes Snowflake, Databricks, BigQuery, Redshift, Azure Synapse
At least 1+ years of experience with reporting tools such PowerBI, Tableau, etc.
Familiarity with SIEM products such as Splunk, Cribl, Elastic, Datadog, AWS CloudTrail, cloud watch, Azure event hub, AWS S3, etc.
Familiarity with streaming platforms like Cribl, Kafka, Kinesis, etc.
Solid knowledge of ETL/ELT pipelines and data ingestion processes
Strong programming skills in Python, SQL and/or Java/Scala and JavaScript
Experience with data modeling, data normalization and schema design
Ability to understand, clean, and transform raw data into structure, usable formats
Experience working with large-scale datasets (batch and streaming)
Experience in analyzing raw data and performing data normalization.
Implementing data validation, quality checks and error handling
Understanding of data governance and metadata management
Strong problem-solving and analytical skills
Must have working knowledge of Artificial Intelligence and Machine Learning technologies. Anthropic, ChatGPT, Gemini, Co-Pilot, etc.
Experience with real-time analytics
Familiarity with BI tools (Power BI, Tableau)
Knowledge of data privacy, compliance and security best practices
Familiar with data standards such as OCSF, OTEL, etc.
Familiar with industry security regulations and frameworks (MITRE Attack Framework, CRI, etc.)
Experience with at least one major cloud provider AWS, Azure or Google
Tech Stack
Amazon Redshift
AWS
Azure
BigQuery
Cloud
DynamoDB
ETL
Java
JavaScript
Kafka
MongoDB
Postgres
Python
Scala
Splunk
SQL
Tableau
Benefits
comprehensive health and wellness benefits
retirement plans
educational assistance and training programs
income replacement for qualified employees with disabilities