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About This Role
About Collective:
Collective is on a mission to redefine the way businesses-of-one work. Our technology and team of trusted advisors help members achieve financial independence by taking care of everything from business incorporation to accounting, bookkeeping, tax services, and access to a thriving community, all in one integrated platform. We believe in empowering self-employed people to enjoy the same tax savings that big companies get, so they can focus on their passion, not paperwork.
Featured in Forbes, Business Insider, Yahoo, Bloomberg, Financial Times, TechCrunch, and more. We are backed by General Catalyst, Sound Ventures, QED Investors, Google’s Gradient Ventures, Expa, and other investors who have financed iconic companies like YouTube, Substack, Twitch, Box, Hims, Instacart, and Lyft.
About the role:
We are looking for a Data Engineer to own and scale the data platform that powers analytics, reporting, and AI across Collective. You will design, build, and maintain the pipelines that move data from our product, financial, and third-party systems into our BigQuery warehouse; model that data into clean, well-documented, reliable tables; and set the engineering standards that keep the platform trustworthy as the company grows.
You will join the Data Engineering team within Engineering and work closely with product engineers, analysts, and business stakeholders across Operations, Finance, and Go-to-Market. This is a hands-on role for someone who cares about data quality, takes ownership of production systems end-to-end, and wants their work to be the foundation the rest of the company builds on.
What you'll do:
- Design and build data pipelines. Develop, deploy, and maintain scalable batch and event-driven pipelines that ingest data from application databases, SaaS tools, and external APIs into BigQuery using managed connectors (Fivetran), custom Python loaders, and orchestration tooling.
- Model the data. Design and implement dimensional and analytical data models in dbt, following a layered architecture (raw, staging, marts) with clear grain, naming conventions, and documentation that analysts and downstream tools can rely on.
- Own data quality and reliability. Implement testing, monitoring, alerting, and data contracts across the pipeline; define and meet freshness and accuracy SLAs; triage and resolve pipeline failures and data incidents to root cause.
- Optimize performance and cost. Tune warehouse queries, partitioning, and clustering; manage BigQuery spend; and keep pipelines efficient as data volume grows.
- Establish engineering standards. Drive best practices for version control, code review, CI/CD, and infrastructure-as-code across the data stack; document systems and runbooks so the platform is maintainable by the team.
- Govern and secure data. Implement access controls, PII handling, and data retention practices appropriate for a financial services company; partner with Security and Legal on compliance requirements.
- Enable the business. Partner with product engineers on source schema design and change management, and with analysts and stakeholders to translate business questions into reliable datasets, metric definitions, and self-serve reporting in Metabase.
- Support AI and analytics use cases. Maintain the semantic layer, metric definitions, and documentation that allow LLM-based tools and internal agents to query the warehouse accurately and consistently.
What you'll bring:
- Experience: 5+ years of professional experience in data engineering, analytics engineering, or a closely related role, ideally at a B2B SaaS or fintech company.
- SQL and Python: Expert-level SQL and strong Python skills for building pipelines, transformations, and tooling; comfortable writing tested, production-grade code.
- Modern data stack: Hands-on production experience with a cloud data warehouse (BigQuery strongly preferred), dbt or equivalent transformation framework, managed ingestion tools (Fivetran or similar), and an orchestrator (Airflow, Dagster, Cloud Composer, or similar).
- Data modeling: Deep understanding of dimensional modeling, layered warehouse architecture, and schema design, with strong opinions on grain, naming, and consistency.
- Data quality and observability: Experience implementing testing frameworks, lineage, monitoring, and alerting for data pipelines, and operating them in production including on-call.
- Engineering fundamentals: Fluency with git-based workflows, code review, CI/CD, and infrastructure-as-code; you treat data infrastructure as software.
- Ownership: A track record of taking ambiguous, high-impact problems and delivering reliable systems end-to-end, with a focus on outcomes rather than just implementation.
- Communication: Ability to explain technical trade-offs to non-technical stakeholders and drive alignment on data definitions across teams.
Nice to have:
- Experience with streaming or event data (Pub/Sub, Kafka, or similar) and product analytics tooling (Amplitude or similar).
- Experience with Terraform and Google Cloud Platform infrastructure.
- Experience with observability platforms such as Datadog.
- Exposure to financial, accounting, tax, or payroll data and the correctness requirements that come with it.
- Experience building semantic layers or metric stores consumed by LLM-based tools, or supporting LLM evaluation programs.
- AI-assisted development experience (Claude Code or similar).
What we offer:
- Hybrid Work Model: Based in San Francisco with a balance of in-office and remote flexibility.
- Fresh Lunch: Provided on in-office days.
- Commuter Support: $150 monthly reimbursement for transit expenses.
- Health & Wellness: $200 quarterly reimbursement to support your well-being.
- Time Off: Flexible PTO plus 14 company holidays.
- Comprehensive Coverage: 100% medical, dental, and vision for employees; 75% coverage for dependents.
- Parental Leave: 16 weeks fully paid.
- Retirement & Ownership: 401k plan plus an equity package.
- Team Connection: Quarterly virtual events and an annual in-person summit.
Compensation Range: $180K - $230K
Salary Context
This $180K-$230K range is above the 75th percentile for Data Engineer roles in our dataset (median: $150K across 25 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 8,943 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Ｃollective, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,825 based on 160 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($205K) sits 15% above the category median. Disclosed range: $180K to $230K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Ｃollective AI Hiring
Ｃollective has 1 open AI role right now. They're hiring across Data Engineer. Based in San Francisco, CA, US. Compensation range: $230K - $230K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% above the national median.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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