Data Engineer

Stuut

. Build and own data infrastructure from the ground up .

Posted 11/3/2026full-timeUnited StatesMid-LevelSenior💰 $150,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling data infrastructure, including designing data pipelines and implementing DataOps best practices. Proficient in data modeling, ensuring data quality, and collaborating with cross-functional teams to deliver strategic insights.

Highest-signal resume keywords

Python ProgrammingETL/ELT WorkflowsData Quality and ObservabilityData Modeling FundamentalsCloud Data Warehousing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentSQLCanonical Data ModelsData TransformationData IngestionMetrics Layer DesignAutomated TestingAnomaly DetectionFeature PipelinesSaaS API Integration

Soft Skills

CollaborationProblem-SolvingAdaptability

Tools & Technologies

SnowflakeBigQueryDbtAirflow

Industry Keywords

FintechB2B SaaSFinancial DataAR/AP Workflows

Tech Stack

Tools & technologies

AirflowBigQueryCloudETLPythonSnowflakeSQL

About the role

Key responsibilities & impact

Build and own data infrastructure from the ground up
Design pipelines ingesting, transforming, and modeling data from customer ERPs, payment processors, and internal systems
Build the transformation and semantic layer as the single source of metric truth
Design canonical data models normalizing heterogeneous source systems, with quality tests and observability
Build event and signal pipelines producing clean, labeled data for analytics, ML, and intelligent product features
Partner with product, engineering, and applied ML teams on data quality, lineage, and observability
Implement DataOps best practices
Collaborate with leadership to define KPIs, build dashboards, and surface strategic insights
Scale the data platform from dozens to hundreds of customers

Requirements

What you’ll need

5+ years of hands-on experience building production data pipelines using Python
Knowledge of SQL and modern cloud data warehouses; experience with Snowflake or BigQuery is a plus
Deep experience implementing ETL/ELT workflows at scale using tools like dbt, Airflow, or similar
Experience building or contributing to a semantic / metrics layer
Understanding of data modeling fundamentals and designing canonical schemas for heterogeneous source data
Experience with real-world data from SaaS APIs, ERPs, and third-party integrations
Strong focus on data quality and observability, including freshness, lineage, automated testing, and anomaly detection
Experience partnering with ML or applied AI teams on feature pipelines or supporting data infrastructure is a bonus, not required
Ability to thrive in ambiguity and build new systems
Experience or strong interest in fintech, B2B SaaS, or financial data; understanding AR/AP workflows is a plus

Benefits

Comp & perks

Top-of-market salary and equity package
Medical, dental & vision insurance coverage for you
401(k) & Match
Equity
Flexible PTO
Parental Leave