# Data Engineer
Location: Denver, CO (Hybrid) · Employment Type: Full-time · Level: Mid-Senior
[Company] is building the analytics platform that helps B2B SaaS companies understand their customers. We turn product usage data, billing events, and CRM records into actionable insights that drive revenue.
Our platform ingests data from 40+ integrations, processes 15 million events daily, and powers dashboards used by customer success teams at 800+ companies. We're a 95-person team backed by $48M in Series B funding from Bessemer and Craft Ventures.
Why join [Company]?
- Work on a modern data stack processing real-scale data (5TB warehouse, 200+ pipelines)
- Join a mature data team that values quality over speed
- Shape data architecture decisions at a critical growth stage
- Competitive compensation with meaningful equity
We're looking for a Data Engineer to join our Data Platform team. You'll build and maintain the pipelines that power our analytics product—from customer data ingestion to the models that feed our dashboards and ML systems.
This role is ideal for someone who's passionate about data quality, loves writing complex SQL, and wants to own pipelines end-to-end. You'll work closely with our data analysts, data scientists, and product engineers to ensure reliable, timely data for 50+ internal and external data consumers.
What makes this role different:
- We're data-mature: dbt, Snowflake, Dagster are already in place—you'll improve, not build from scratch
- Data quality is non-negotiable: we catch bad data before stakeholders see it
- Real impact: the pipelines you build directly power customer-facing features
- Ensure 99.5%+ pipeline reliability across our 200+ scheduled jobs
- Reduce data freshness from 4 hours to under 1 hour for critical tables
- Improve query performance for analyst-facing models by 50%
- Establish data contracts between product engineering and the data platform
- Mentor junior data engineers and contribute to team knowledge sharing
- Design and implement ELT pipelines ingesting data from production databases, SaaS APIs, and event streams
- Build and optimize dbt models following dimensional modeling best practices
- Write and optimize complex SQL—window functions, CTEs, incremental models, query plan analysis
- Implement data quality checks using dbt tests and Great Expectations to catch anomalies early
- Debug pipeline failures, conduct root cause analysis, and implement preventive measures
- Partner with analysts to understand their data needs and optimize query patterns
- Collaborate with data scientists on feature engineering pipelines and training datasets
- Maintain data documentation, lineage tracking, and impact analysis for schema changes
- Participate in on-call rotation for data incidents (1 week every 5 weeks)
- 4+ years of professional data engineering experience
- SQL mastery (non-negotiable): Expert-level SQL including complex joins across 10+ tables, window functions (LAG, LEAD, RANK, NTILE), CTEs, and recursive queries
- Proficiency analyzing query execution plans and optimizing slow queries (indexing, partitioning, clustering)
- Strong Python skills for pipeline development, data transformation, and automation
- Hands-on experience with dbt or similar transformation tools
- Experience with workflow orchestration tools (Airflow, Dagster, or Prefect)
- Strong understanding of dimensional modeling (star schemas, slowly changing dimensions)
- Familiarity with data quality testing and monitoring practices
- Comfortable writing SQL that processes millions of records efficiently
- Experience with Snowflake specifically (our primary warehouse)
- Familiarity with Dagster (our orchestration tool)
- Background in B2B SaaS or product analytics
- Experience with streaming data (Kafka, Kinesis)
- Familiarity with data lake architectures (Delta Lake, Iceberg)
- Experience with Great Expectations or Monte Carlo for data quality
- Infrastructure as code experience (Terraform)
- Contributions to open-source data tools or technical writing
- Data Warehouse: Snowflake
- Orchestration: Dagster (migrating from Airflow)
- Transformation: dbt Core
- Ingestion: Fivetran, custom Python connectors, Kafka
- Data Quality: dbt tests, Great Expectations, Monte Carlo
- Data Catalog: Atlan
- BI/Analytics: Looker, Hex
- Cloud Platform: AWS (S3, Lambda, ECS)
- Version Control: GitHub
- CI/CD: GitHub Actions, dbt Cloud
- Warehouse Size: 5TB (growing 20% quarterly)
- Daily Events Processed: 15 million
- Data Sources: 40+ integrations (databases, APIs, event streams)
- Scheduled Pipelines: 200+ dbt models and ingestion jobs
- Data Consumers: 50+ users (analysts, scientists, customer dashboards)
- Query Volume: 25,000 queries/day
- Freshness SLA: 4 hours (goal: 1 hour)
Salary: $145,000 - $185,000 (based on experience and location)
Equity: 0.03% - 0.10% (4-year vest, 1-year cliff)
Benefits:
- Medical, dental, and vision insurance (100% employee, 80% dependents)
- Unlimited PTO with 15-day minimum encouraged
- $3,500 annual learning budget (conferences, courses, certifications)
- $1,500 home office setup allowance
- 401(k) with 4% company match
- 16 weeks paid parental leave
- Annual data conference stipend (dbt Coalesce, Data Council, etc.)
- Flexible hybrid work (2 days in Denver office, remote-friendly for strong candidates)
Our interview process typically takes 2-3 weeks. We focus on real data engineering skills.
- Step 1: Recruiter Screen (30 min) - We'll discuss your background, interests, and compensation expectations.
- Step 2: SQL Assessment (60 min) - Live SQL session with complex queries, window functions, and optimization scenarios.
- Step 3: Pipeline Design (60 min) - Design a data pipeline including ingestion, transformation, and quality checks.
- Step 4: Technical Deep-Dive (45 min) - Past projects, dbt experience, and data quality strategies.
- Step 5: Team Interviews (2 x 30 min) - Meet data consumers like analysts and scientists.
- Step 6: Hiring Manager (30 min) - Career goals, team fit, and offer discussion.
We provide written feedback if you reach the SQL assessment.
Submit your resume and optionally include links to GitHub, dbt projects, or technical writing. We'd love to see examples of data models or pipelines you're proud of.
---
*[Company] is an equal opportunity employer. We're committed to building a diverse team and inclusive culture. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status.*
*We encourage applications from candidates who may not meet 100% of the qualifications. Research shows underrepresented groups are less likely to apply unless they meet every requirement—we'd rather you apply and let us decide.*
# Data Engineer
**Location:** Denver, CO (Hybrid) · **Employment Type:** Full-time · **Level:** Mid-Senior
## About [Company]
[Company] is building the analytics platform that helps B2B SaaS companies understand their customers. We turn product usage data, billing events, and CRM records into actionable insights that drive revenue.
Our platform ingests data from 40+ integrations, processes 15 million events daily, and powers dashboards used by customer success teams at 800+ companies. We're a 95-person team backed by $48M in Series B funding from Bessemer and Craft Ventures.
**Why join [Company]?**
- Work on a modern data stack processing real-scale data (5TB warehouse, 200+ pipelines)
- Join a mature data team that values quality over speed
- Shape data architecture decisions at a critical growth stage
- Competitive compensation with meaningful equity
## The Role
We're looking for a Data Engineer to join our Data Platform team. You'll build and maintain the pipelines that power our analytics product—from customer data ingestion to the models that feed our dashboards and ML systems.
This role is ideal for someone who's passionate about data quality, loves writing complex SQL, and wants to own pipelines end-to-end. You'll work closely with our data analysts, data scientists, and product engineers to ensure reliable, timely data for 50+ internal and external data consumers.
**What makes this role different:**
- We're data-mature: dbt, Snowflake, Dagster are already in place—you'll improve, not build from scratch
- Data quality is non-negotiable: we catch bad data before stakeholders see it
- Real impact: the pipelines you build directly power customer-facing features
## Objectives of This Role
- Ensure 99.5%+ pipeline reliability across our 200+ scheduled jobs
- Reduce data freshness from 4 hours to under 1 hour for critical tables
- Improve query performance for analyst-facing models by 50%
- Establish data contracts between product engineering and the data platform
- Mentor junior data engineers and contribute to team knowledge sharing
## Responsibilities
- Design and implement ELT pipelines ingesting data from production databases, SaaS APIs, and event streams
- Build and optimize dbt models following dimensional modeling best practices
- Write and optimize complex SQL—window functions, CTEs, incremental models, query plan analysis
- Implement data quality checks using dbt tests and Great Expectations to catch anomalies early
- Debug pipeline failures, conduct root cause analysis, and implement preventive measures
- Partner with analysts to understand their data needs and optimize query patterns
- Collaborate with data scientists on feature engineering pipelines and training datasets
- Maintain data documentation, lineage tracking, and impact analysis for schema changes
- Participate in on-call rotation for data incidents (1 week every 5 weeks)
## Required Skills and Qualifications
- 4+ years of professional data engineering experience
- **SQL mastery (non-negotiable):** Expert-level SQL including complex joins across 10+ tables, window functions (LAG, LEAD, RANK, NTILE), CTEs, and recursive queries
- Proficiency analyzing query execution plans and optimizing slow queries (indexing, partitioning, clustering)
- Strong Python skills for pipeline development, data transformation, and automation
- Hands-on experience with dbt or similar transformation tools
- Experience with workflow orchestration tools (Airflow, Dagster, or Prefect)
- Strong understanding of dimensional modeling (star schemas, slowly changing dimensions)
- Familiarity with data quality testing and monitoring practices
- Comfortable writing SQL that processes millions of records efficiently
## Preferred Skills and Qualifications
- Experience with Snowflake specifically (our primary warehouse)
- Familiarity with Dagster (our orchestration tool)
- Background in B2B SaaS or product analytics
- Experience with streaming data (Kafka, Kinesis)
- Familiarity with data lake architectures (Delta Lake, Iceberg)
- Experience with Great Expectations or Monte Carlo for data quality
- Infrastructure as code experience (Terraform)
- Contributions to open-source data tools or technical writing
## Tech Stack
- **Data Warehouse:** Snowflake
- **Orchestration:** Dagster (migrating from Airflow)
- **Transformation:** dbt Core
- **Ingestion:** Fivetran, custom Python connectors, Kafka
- **Data Quality:** dbt tests, Great Expectations, Monte Carlo
- **Data Catalog:** Atlan
- **BI/Analytics:** Looker, Hex
- **Cloud Platform:** AWS (S3, Lambda, ECS)
- **Version Control:** GitHub
- **CI/CD:** GitHub Actions, dbt Cloud
## Data Scale
- **Warehouse Size:** 5TB (growing 20% quarterly)
- **Daily Events Processed:** 15 million
- **Data Sources:** 40+ integrations (databases, APIs, event streams)
- **Scheduled Pipelines:** 200+ dbt models and ingestion jobs
- **Data Consumers:** 50+ users (analysts, scientists, customer dashboards)
- **Query Volume:** 25,000 queries/day
- **Freshness SLA:** 4 hours (goal: 1 hour)
## Compensation and Benefits
**Salary:** $145,000 - $185,000 (based on experience and location)
**Equity:** 0.03% - 0.10% (4-year vest, 1-year cliff)
**Benefits:**
- Medical, dental, and vision insurance (100% employee, 80% dependents)
- Unlimited PTO with 15-day minimum encouraged
- $3,500 annual learning budget (conferences, courses, certifications)
- $1,500 home office setup allowance
- 401(k) with 4% company match
- 16 weeks paid parental leave
- Annual data conference stipend (dbt Coalesce, Data Council, etc.)
- Flexible hybrid work (2 days in Denver office, remote-friendly for strong candidates)
## Interview Process
Our interview process typically takes 2-3 weeks. We focus on real data engineering skills.
- **Step 1: Recruiter Screen** (30 min) - We'll discuss your background, interests, and compensation expectations.
- **Step 2: SQL Assessment** (60 min) - Live SQL session with complex queries, window functions, and optimization scenarios.
- **Step 3: Pipeline Design** (60 min) - Design a data pipeline including ingestion, transformation, and quality checks.
- **Step 4: Technical Deep-Dive** (45 min) - Past projects, dbt experience, and data quality strategies.
- **Step 5: Team Interviews** (2 x 30 min) - Meet data consumers like analysts and scientists.
- **Step 6: Hiring Manager** (30 min) - Career goals, team fit, and offer discussion.
We provide written feedback if you reach the SQL assessment.
## How to Apply
Submit your resume and optionally include links to GitHub, dbt projects, or technical writing. We'd love to see examples of data models or pipelines you're proud of.
---
*[Company] is an equal opportunity employer. We're committed to building a diverse team and inclusive culture. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status.*
*We encourage applications from candidates who may not meet 100% of the qualifications. Research shows underrepresented groups are less likely to apply unless they meet every requirement—we'd rather you apply and let us decide.*