Senior Data Engineer at Cortex

Job description

🧠 About Cortex Cortex is the Engineering Operations Platform - mission control for the AI software factory. As AI multiplies how much teams ship, code stops being the bottleneck and everything around it becomes the constraint. Cortex gives engineering organizations the visibility, governance, and golden paths to move fast without runaway risk to reliability and security - across Catalog and Context Graph, Scorecards, Engineering Intelligence, Self-Service and Workflows, and AI Impact. We're a YC company, SOC 2 Type II and ISO/IEC 27001 certified, used by teams like Rapid7, Xero, BigCommerce, Skyscanner, LetsGetChecked, and H&R Block. AI-augmented engineering is the floor here, not a side experiment - Cortex engineers build on Cortex every day. 📍Location We’re fully remote and welcome candidates from anywhere in the US! We have all-company offsites a few times a year where we fly the whole team out to meet in person, build stronger relationships, kick off important projects, and have fun! 🤝 The Team We are a mighty group of 80 passionate individuals excited about building a product that developers love. We recently raised $60M in Series C Funding this year led by Scale Ventures (with participation from Sequoia, IVP, and others) to build the Future of Developer Experience. You can read more about it here . 💼 Role Summary As a Senior Data Engineer , you’ll own our internal data systems and pipelines. You will be responsible for the ingestion, transformation, and warehousing that power reporting across the company - consolidating a stack currently spread across Segment, Hevo, BigQuery, and Omni into a single, trustworthy source of truth, and laying the foundation for higher-volume data work as we grow. You'll partner closely with GTM operations to ensure our AI-driven automation & tools areis built on accurate, well-modeled data, including you'll own product analytics. 🛠️ Responsibilities Own data pipelines end to end - ingestion, transformation (dbt), warehousing (BigQuery), and delivery into reporting and BI (Omni) Audit and map the existing data systems before rebuilding, then consolidate the stack toward a single source of truth Build and maintain dbt models and transformation logic with tests, documentation, and clear contracts Establish data quality, observability, and reliability practices so pipelines are maintained intentionally rather than reactively Own product analytics: instrumentation and event tracking, data models for usage and adoption, and the metrics GTM and product teams rely on Partner with GTM operations to consolidate reporting into a single tool and ensure we have the right data foundations for AI automation and tools. Support research for external reports (e.g. the Engineering in the Age of AI benchmark report), partnering with marketing to source, validate, and pull the right data Lay the groundwork for high-volume and streaming ingestion (e.g. telemetry / OpenTelemetry) as the product moves that way ✔️Qualifications Bachelor's degree in Computer Science or related field, or equivalent practical experience 4+ years of hands-on data engineering experience building and owning production pipelines Proficiency with dbt, a cloud data warehouse (BigQuery or equivalent), and ETL/ELT tooling Strong SQL and proficiency in a general-purpose language (e.g. Python) for pipeline and transformation work Solid data modeling judgment with a focus on data quality, testing, and reliability A bias toward mapping and rationalizing a messy environment before adding to it Strong communication and collaboration skills, including with non-engineering stakeholders Fluent, critical use of AI in day-to-day engineering, reviewing AI output as carefully as a teammate's PR Experience with high-volume / streaming systems (ClickHouse, OpenTelemetry) or CDP / ETL tools (Segment, Hevo) is a plus Previous experience at a startup is a plus 💰 Compensation Starting Salary Range: $175,000.00 to $205,000.00 Competitive Equity Package * Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for equity and benefits. 🌴 Perks & Benefits National medical, dental & vision insurance (we cover 100% for employees!) Unlimited PTO & flexible working hours 401k plan Gender-neutral parental leave $1000 yearly Learning & Development stipend $400 monthly perks stipend (use it on anything) Pet Insurance Cortex is committed to a diverse and inclusive workplace. Cortex is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please email people@cortex.io

Requirements

Must-haves

Own data pipelines end to end - ingestion, transformation (dbt), warehousing (BigQuery), and delivery into reporting and BI (Omni)
Audit and map the existing data systems before rebuilding, then consolidate the stack toward a single source of truth
Build and maintain dbt models and transformation logic with tests, documentation, and clear contracts
Establish data quality, observability, and reliability practices so pipelines are maintained intentionally rather than reactively
Own product analytics: instrumentation and event tracking, data models for usage and adoption, and the metrics GTM and product teams rely on
Partner with GTM operations to consolidate reporting into a single tool and ensure we have the right data foundations for AI automation and tools.
Support research for external reports (e.g. the Engineering in the Age of AI benchmark report), partnering with marketing to source, validate, and pull the right data
Lay the groundwork for high-volume and streaming ingestion (e.g. telemetry / OpenTelemetry) as the product moves that way
Bachelor's degree in Computer Science or related field, or equivalent practical experience
4+ years of hands-on data engineering experience building and owning production pipelines
Proficiency with dbt, a cloud data warehouse (BigQuery or equivalent), and ETL/ELT tooling
Strong SQL and proficiency in a general-purpose language (e.g. Python) for pipeline and transformation work
Solid data modeling judgment with a focus on data quality, testing, and reliability
A bias toward mapping and rationalizing a messy environment before adding to it
Strong communication and collaboration skills, including with non-engineering stakeholders
Fluent, critical use of AI in day-to-day engineering, reviewing AI output as carefully as a teammate's PR

Nice-to-haves

Experience with high-volume / streaming systems (ClickHouse, OpenTelemetry) or CDP / ETL tools (Segment, Hevo) is a plus
Previous experience at a startup is a plus

Application prep

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Keywords to mirror

aisqlpython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Pto

Parental Leave