Data Engineer
Build and own Stuut’s foundational data platform, including ingestion pipelines, canonical models, semantic layers, and observability. The role requires 3+ years of production data pipeline experience with Python, SQL, cloud warehouses, and ETL/ELT tooling.
About the job
Responsibilities
- Build and own data infrastructure, including pipelines that ingest, transform, and model data from customer ERPs, payment processors, and internal systems.
- Build the transformation and semantic layer as the source of truth for metrics across customer analytics, internal reporting, and AI/ML systems.
- Design canonical data models that normalize heterogeneous source systems, with quality tests and observability.
- Build event and signal pipelines that produce clean, labeled data for analytics, machine learning, and intelligent product features.
- Partner with product, engineering, and applied ML teams to embed data quality, lineage, and observability.
- Implement DataOps practices to keep data and AI features timely, accurate, and trusted.
- Define KPIs, build dashboards, and surface insights for strategic decisions.
- Scale the data platform as the customer base grows.
Requirements
- 3+ years of hands-on experience building production data pipelines with Python.
- Strong SQL skills and experience with modern cloud data warehouses.
- Deep experience implementing ETL/ELT workflows at scale.
- Experience with data modeling and designing canonical schemas for heterogeneous source data.
- Experience working with SaaS APIs, ERPs, and third-party integrations.
- Strong understanding of data quality and observability, including freshness, lineage, automated testing, and anomaly detection.
- Ability to work in an ambiguous, early-stage environment and build systems from scratch.
Nice-to-haves
- Experience with Snowflake or BigQuery.
- Experience with dbt, Airflow, or similar workflow and transformation tools.
- Experience building semantic or metrics layers.
- Experience partnering with ML or applied AI teams on feature pipelines or data infrastructure.
- Experience or strong interest in fintech, B2B SaaS, or financial data; familiarity with AR/AP workflows.
Compensation and Benefits
- Top-of-market salary and equity package.
- Medical, dental, and vision insurance for U.S.-based full-time employees.
- 401(k) with match.
- Equity.
- Flexible PTO.
- Parental leave.
Skills
Python, SQL, Snowflake, BigQuery, ETL, ELT, dbt, Airflow, Data Modeling, Data Warehousing, Data Quality, Data Observability, Data Lineage, Machine Learning
Similar jobs
Data Engineering jobs
Ontic
United States
Build scalable analytics engineering infrastructure, SaaS data models, and AI-enabled workflows that support enterprise decision-making. The role requires 3–6 years of hands-on analytics or data engineering experience, strong SQL and modern data modeling expertise, and cloud data warehouse experience.
Underdog Fantasy
United States
Build and operate the data platform supporting automated regulatory reporting for a prediction markets business. The role combines SQL and dbt development, end-to-end data investigation, automated validation, and cross-functional ownership under strict deadlines.
Attentive
United States
Build and operate distributed data applications powering large-scale audience segmentation and real-time personalization. The role requires 2–4 years of software engineering experience, backend development skills, and familiarity with databases, algorithms, and distributed systems.
Bevi
Boston, MA
Build and maintain dbt models, Snowflake semantic layers, and ingestion pipelines across business functions while improving data quality and resilience. The role requires 4–6 years of analytics or data engineering experience, strong dbt and SQL expertise, and a quantitative bachelor's degree.
Sigma
New York, NY
Build and scale data architecture, governance, and ETL pipelines across Snowflake, Databricks, and cloud platforms. The role requires 3+ years of data engineering experience, strong API and pipeline expertise, and the ability to collaborate across technical and business teams.