AI Data Engineer, Data Platform

Collective

. Design, build, deploy, and maintain scalable batch and event-driven data pipelines .

Posted 9/24/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $180,000 - $230,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and maintaining scalable data pipelines, with a strong focus on BigQuery, SQL, and Python. Proficient in implementing data models, monitoring frameworks, and compliance practices while collaborating with cross-functional teams to deliver reliable data solutions.

Highest-signal resume keywords

BigQuerySQLPythonData Pipeline ManagementDbt

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringDimensional ModelingProduction-Grade CodeTesting FrameworksData IngestionCloud Data WarehouseData MonitoringData Retention PracticesVersion ControlInfrastructure-as-Code

Soft Skills

Problem-SolvingCommunicationCollaboration

Tools & Technologies

FivetranAirflowMetabaseTerraformGoogle Cloud Platform

Industry Keywords

B2B SaaSFintechComplianceData GovernanceData Analytics

Tech Stack

Tools & technologies

AirflowBigQueryCloudGoogle Cloud PlatformKafkaPythonSQLTerraform

About the role

Key responsibilities & impact

Design, build, deploy, and maintain scalable batch and event-driven data pipelines
Ingest data from application databases, SaaS tools, and external APIs into BigQuery using Fivetran, custom Python loaders, and orchestration tooling
Design and implement dimensional and analytical data models in dbt using raw, staging, and marts layers
Implement testing, monitoring, alerting, and data contracts across pipelines
Define and meet freshness and accuracy SLAs
Triage and resolve pipeline failures and data incidents to root cause
Tune warehouse queries, partitioning, and clustering
Manage BigQuery spend and pipeline efficiency as data volume grows
Establish version control, code review, CI/CD, and infrastructure-as-code best practices
Document systems and runbooks
Implement access controls, PII handling, and data retention practices
Partner with Security and Legal on compliance requirements
Partner with product engineers on source schema design and change management
Translate business questions into reliable datasets, metric definitions, and self-serve reporting in Metabase
Maintain the semantic layer, metric definitions, and documentation supporting accurate LLM-based tools and internal agents

Requirements

What you’ll need

5+ years of professional experience in data engineering, analytics engineering, or a closely related role
Experience ideally at a B2B SaaS or fintech company
Expert-level SQL skills
Strong Python skills for pipelines, transformations, and tooling
Experience writing tested, production-grade code
Production experience with a cloud data warehouse; BigQuery strongly preferred
Experience with dbt or an equivalent transformation framework
Experience with managed ingestion tools such as Fivetran or similar
Experience with an orchestrator such as Airflow, Dagster, Cloud Composer, or similar
Deep understanding of dimensional modeling, layered warehouse architecture, and schema design
Experience implementing testing frameworks, lineage, monitoring, and alerting for data pipelines
Experience operating data pipelines in production, including on-call
Fluency with git-based workflows, code review, CI/CD, and infrastructure-as-code
Track record of taking ambiguous, high-impact problems and delivering reliable systems end-to-end
Ability to explain technical trade-offs to non-technical stakeholders and drive alignment on data definitions
Nice to have: streaming or event data experience with Pub/Sub, Kafka, or similar
Nice to have: product analytics tooling experience such as Amplitude
Nice to have: Terraform and Google Cloud Platform infrastructure experience
Nice to have: observability platforms such as Datadog
Nice to have: financial, accounting, tax, or payroll data exposure
Nice to have: semantic layers or metric stores consumed by LLM-based tools, or LLM evaluation programs
Nice to have: AI-assisted development experience such as Claude Code
Legally authorized to work in the United States for any employer

Benefits

Comp & perks

Equity package
Bonus
Fresh lunch provided on in-office days
$150 monthly reimbursement for transit expenses
$200 quarterly reimbursement for well-being
Flexible PTO plus 14 company holidays
100% medical, dental, and vision coverage for employees
75% medical, dental, and vision coverage for dependents
16 weeks fully paid parental leave
401(k) plan
Quarterly virtual events
Annual in-person summit
Hybrid work model with in-office and remote flexibility