Senior Data Engineer

Company Overview

TENEX is an AI-native, automation-first, built-for-scale Managed Detection and Response (MDR) provider. We are a force multiplier for defenders, helping organizations enhance their cybersecurity posture through advanced threat detection, rapid response, and continuous protection. Our team is composed of industry experts with deep experience in cybersecurity, automation, and AI-driven solutions.

We’re a fast-growing startup backed by Andreessen Horowitz. As an early employee, you’ll help shape our culture and have meaningful ownership over high-impact initiatives. This is a unique opportunity to join a small but well-funded team on the ground floor as we build the next-generation cybersecurity platform.

We are expanding our engineering organization and seeking a Senior Data Engineer to architect and own our data infrastructure. This role is foundational in enabling high-quality analytics, reporting, and AI-driven insights across TENEX.

Culture is one of the most important things at TENEX.AI—explore our culture deck at culture.tenex.ai to witness how we embody it, prioritizing the irreplaceable collaboration and community of in-person work. This is an in-person opportunity based in our San Jose, CA office where you will be expected to work out of 4 days a week.

Role Overview

As a Senior Data Engineer, you will design, build, and scale the data systems that power our cybersecurity platform and internal decision-making. You will own ETL/ELT architecture, develop our data warehouse, define core metrics, and enable analytics for engineering, product, security operations, and leadership teams.

You’ll work across the stack—from ingestion pipelines to transformations to reporting—ensuring our data is reliable, actionable, and trustworthy. This role blends hands-on engineering with strategic influence over how TENEX collects, processes, models, and uses data.

Job Responsibilities:

Data Architecture & Pipelines

• Design, build, and maintain scalable ETL/ELT pipelines for ingesting and processing large volumes of cybersecurity data.

• Develop and manage data warehouse architectures using platforms such as Snowflake, BigQuery, or Redshift.

• Build robust data models and schemas that support analytics, product features, and machine learning workflows.

• Ensure the reliability, scalability, and performance of data infrastructure.

Analytics Enablement

• Define, instrument, and maintain core business and product metrics across the organization.

• Build data marts, semantic layers, and curated datasets for use across engineering, product, operations, and customer-facing analytics.

• Partner with stakeholders to identify data needs and translate them into high-impact data solutions.

Dashboards & Reporting

• Develop dashboards, automated reporting, and analytics layers using modern BI tools.

• Drive visibility into platform performance, security outcomes, and operational metrics.

• Ensure teams have easy access to accurate, timely insights.

Data Quality & Governance

• Establish data validation best practices, monitoring, lineage, and observability.

• Implement automated processes to ensure accuracy, consistency, and resilience across pipelines.

• Maintain clear documentation, metadata, and schema evolution practices.

Cross-Functional Collaboration

• Collaborate closely with engineering, product, and security teams to support new features and data-driven capabilities.

• Provide technical input into product design, data instrumentation, and architecture.

• Advocate for data best practices across the organization.

Continuous Improvement

• Evaluate and integrate tools that improve data performance, reliability, and automation.

• Contribute to engineering excellence through tooling, CI/CD for data, testing approaches, and process improvements.

Required Skills & Qualifications:

• 5+ years of professional experience in data engineering or equivalent.

• Strong experience building ETL/ELT pipelines and distributed data processing systems.

• Hands-on experience with cloud data warehouses such as Snowflake, BigQuery, or Redshift.

• Expertise with SQL and relational databases (PostgreSQL, MySQL, etc.).

• Proficiency in at least one data engineering language (Python, Go, Scala, etc.).

• Experience working with cloud platforms (GCP or AWS) and cloud-native data services.

• Experience with orchestration and workflow systems (Airflow, Dagster, Prefect, or similar).

• Strong understanding of data modeling, warehousing principles, and performance tuning.

• Demonstrated ability to own complex projects end-to-end with minimal oversight.

• Excellent communication, collaboration, and problem-solving skills.

Desired:

• Experience with modern analytics engineering frameworks.

• Experience with real-time / streaming data systems (Kafka, Pub/Sub, Kinesis).

• Familiarity with BI & dashboarding tools (Looker, Grafana, etc.).

• Experience preparing datasets for AI/ML workflows, including:

  • Feature engineering

  • Vector databases

  • RAG pipelines

• Prior experience in cybersecurity, security analytics, or security data modeling.

• Experience working in an early-stage startup environment.

Education & Certifications

• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).

• Cloud certifications (GCP Data Engineer, AWS Data Analytics Specialty, or similar) are a plus.

Why Join Us?

• Opportunity to work with cutting-edge AI-driven cybersecurity technologies and Google SecOps solutions.

• Collaborate with a talented and innovative team focused on continuously improving security operations.

• Competitive salary and benefits package.

• A culture of growth and development, with opportunities to expand your knowledge in AI, cybersecurity, and emerging technologies.