Senior Data Engineer - US

Teleport

6d•$222,000 - $342,000•Remote

About The Position

We are looking for our first Data Engineer to build Teleport's internal data platform. You will design and own the pipelines and warehouse that turn raw data from across the company, including product usage, production costs, budgets, and sales, into data that any team can use to answer their own questions. You will work closely with teams across the company, including product, engineering, finance, and revenue, to understand their data needs and deliver reliable, well-modeled data they can depend on.

Requirements

Willingness to work collaboratively with Teleport's engineers on Data Engineering Challenge to design and build an analytics warehouse for a fictional business.
Builder mentality; experience building a data engineering function from scratch at a growing startup is highly desirable.
Strong SQL and dimensional or analytical data-modeling expertise.
Experience translating ambiguous stakeholder questions into documented, testable business definitions.
Production experience with dbt or an equivalent SQL transformation workflow.
Experience with a cloud data warehouse such as Redshift, Snowflake, or BigQuery.
Experience with managed ELT, APIs, object storage, incremental loading, and source schema evolution.
Strong data-quality and reconciliation practices, including explaining how published numbers relate to their sources.
Experience handling sensitive data, access control, and retention responsibly.
Practical experience with Python or Go, CI/CD, infrastructure as code, and cloud operations.
Sound judgment about build vs. buy decisions, operational simplicity, and managing scope in a startup environment.
Intellectual curiosity and a willingness to master new technologies.
Comfortable changing the area of focus and working directly with stakeholders across the company.
No-ego mindset of collaboration, transparency, and seeking feedback from others.

Responsibilities

Design and operate a low-maintenance cloud data warehouse and transformation workflow.
Evaluate managed connectors, source exports, and native cloud services before building custom ingestion software.
Model data from CRM, contracts, finance, billing, product usage, and cloud infrastructure into coherent business entities and metrics.
Establish canonical customer identity and mappings between accounts, tenants, billing entities, and other source-specific identifiers.
Partner with Finance, Revenue, Product, and Engineering to define concepts such as ARR, adoption, infrastructure cost, and gross margin.
Build data quality checks, reconciliation, documentation, lineage, and observable failure behavior into the system.
Protect customer and employee information through appropriate access control, minimization, redaction, and retention practices.
Manage warehouse performance and cost, and keep the platform understandable and operable by a small team.
Use Python or Go for integrations that cannot be handled safely and economically by managed or warehouse-native capabilities.