Edtech.com's Summary

Brightwheel is hiring a Staff Data Engineer to lead the development and scaling of its data platform, focusing on advanced data pipelines and AI/ML integration. The role involves architecting data infrastructure, building AI-powered systems, and mentoring engineers to enhance data-driven products and insights.

Highlights

Architect and evolve a scalable data platform supporting analytics and AI/ML workloads
Design and implement production pipelines for large language models (LLMs) and intelligent operations
Manage end-to-end data acquisition and integration from diverse sources ensuring quality and governance
Create reusable AI tooling and abstractions to standardize engineering efforts across teams
Lead technical decisions on infrastructure, tooling, and data architecture with enterprise-wide impact
Mentor and provide technical guidance to engineers, promoting best practices in data engineering
Proficient in Python and modern data engineering methods with 6+ years experience
Hands-on experience deploying AI/LLM-based features and ML pipelines at scale in production environments
Expertise in distributed data processing technologies like Spark, Redshift, BigQuery, Snowflake, and storage formats such as Parquet and ORC
Compensation ranges from $154K to $237K plus equity and includes comprehensive benefits such as medical, dental, vision, generous PTO, and parental leave

Staff Data Engineer Full Description

Staff Data Engineer

United States

Employment Type

Full time

Location Type

Remote

Department

Engineering

Compensation

$154K – $237K • Offers Equity

Compensation & Benefits
At brightwheel, we reward people who make things happen. Our compensation is benchmarked against similar-stage growth companies, with ranges set by function, level, and location. Final offers reflect experience and expertise, and your recruiter can walk you through the range for your region.

Equity & Ownership
Many roles include equity, giving you the chance to share in the long-term success you help create.

Benefits & Well-Being
We design our benefits to help you (and your family) live well, stay healthy, and keep growing. While specifics vary by region, we typically offer:

Comprehensive medical, dental, and vision coverage
Generous paid parental leave
Flexible PTO so you can recharge when you need it
Local retirement or savings plans (e.g., 401(k) in the U.S.)

We’re committed to pay equity and to building a diverse, inclusive workplace where everyone can do their best work and feel supported along the way.

Overview
Application
Our Mission and Opportunity
Early education is one of the most important determinants of childhood outcomes, a critical support for working families, and a $175B market that remains underserved by modern technology. Brightwheel is the largest, fastest growing, and most loved platform in early ed, trusted by millions of educators and families every day. We are a three-time Cloud 100 company, backed by top investors including Addition, Bessemer, Emerson Collective, Lowercase Capital, Notable Capital, and Mark Cuban.

Our Team
Our team is passionate, talented, and customer-focused. We embody our Leadership Principles in our work and culture. We are a distributed team with remote employees across every US time zone, as well as select offices in the US and internationally.

Who You Are
Brightwheel is seeking a Staff Data Engineer and technical lead on our Data Engineering team. As a Staff Data Engineer at brightwheel, you will architect and drive the evolution of our data platform, partnering with technical leadership to shape our data and AI strategy. You will design and scale sophisticated data pipelines processing billions of records across diverse systems, powering analytics for internal teams, customer-facing insights, and AI/ML capabilities that differentiate our product.
You are a technical leader with deep data engineering expertise. You are passionate about the intersection of data engineering and AI. You thrive on complex architectural challenges and have the vision to balance immediate business needs with long-term platform scalability. You excel at driving technical decisions and influencing across the organization to deliver high-impact outcomes.

You are a curious, strategic thinker who takes full ownership of critical initiatives with enterprise-wide visibility. You navigate ambiguity effectively, juggle competing priorities, and have the technical depth and communication skills to drive consensus on complex problems. You're excited to shape the future of data and AI at brightwheel while delivering measurable value to our customers and business.

What You’ll Do

Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale
Design and build production LLM pipelines and infrastructure that power intelligent operations.
Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance
Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation.
Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened.
Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features.
Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

Required Qualifications, Skills, & Abilities

6+ years of work experience as a data engineer, backend engineer, full stack or DevOps engineer with strong proficiency in Python and modern data engineering practices
Applied AI impact at scale: Proven track record of shipping AI / LLM-powered features into production with clear, measurable impact on key metrics (for example, engagement, time saved, satisfaction, or retention), ideally across more than one product area.
Hands-on experience with large language models (LLMs) in real applications, including prompt and tool design, retrieval-style patterns (such as RAG), and evaluation and monitoring in production.
Strong computer science fundamentals (e.g., data structures, algorithms, and systems design) and a generalist mindset, comfortable moving between backend, data, and UX to get the job done.
Experience designing, developing, and deploying ML/LLM/AI pipelines in production environments, including experience with model serving, feature engineering, and MLOps practices
Expert-level understanding of distributed data processing technologies and their internals (e.g., Spark execution model, query optimization in Redshift/BigQuery/Snowflake, storage formats like Parquet/ORC)
Proven track record of independently architecting scalable data solutions, from requirements gathering and technical design through implementation and cost optimization, with focus on long-term maintainability and ROI

Preferred Experience

Proven track record of technical leadership, including mentoring senior engineers, driving engineering standards and best practices, and influencing data platform strategy across the organization
Hands-on experience architecting federated query engines (DuckDB, Trino, Presto, Starburst) over lakehouse platforms, including catalog integration (Glue, Iceberg, Hudi), query optimization strategies, and cost-effective compute scaling patterns
Deep expertise building orchestration platforms with Airflow (or similar), including custom operators, dynamic DAG generation, and framework-level optimizations for complex dependency management
Advanced experience with serverless and event-driven architectures, including designing systems that leverage AWS Lambda, Step Functions, EventBridge, or Databricks workflows for cost-efficient, auto-scaling data processing
Experience building customer-facing embedded analytics solutions (Cube.js, Metabase, Superset, or similar) with complex data modeling, access control, and performance optimization

Brightwheel is committed to creating a diverse and inclusive work environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Protecting Our Applicants: Please be aware of recruiting scams impersonating Brightwheel. All legitimate communications come from @mybrightwheel.com addresses, and we never ask for payment or sensitive personal data as part of our hiring process. If you suspect fraudulent contact, reach out to security@mybrightwheel.com. Thank you for helping us keep our applicant community safe.

Original Job Description

Staff Data Engineer

United States

Employment Type

Full time

Location Type

Remote

Department

Engineering

Compensation

$154K – $237K • Offers Equity

Equity & Ownership
Many roles include equity, giving you the chance to share in the long-term success you help create.

Benefits & Well-Being
We design our benefits to help you (and your family) live well, stay healthy, and keep growing. While specifics vary by region, we typically offer:

Comprehensive medical, dental, and vision coverage
Generous paid parental leave
Flexible PTO so you can recharge when you need it
Local retirement or savings plans (e.g., 401(k) in the U.S.)

We’re committed to pay equity and to building a diverse, inclusive workplace where everyone can do their best work and feel supported along the way.

Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale
Design and build production LLM pipelines and infrastructure that power intelligent operations.
Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance
Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation.
Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened.
Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features.
Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

Required Qualifications, Skills, & Abilities

6+ years of work experience as a data engineer, backend engineer, full stack or DevOps engineer with strong proficiency in Python and modern data engineering practices
Applied AI impact at scale: Proven track record of shipping AI / LLM-powered features into production with clear, measurable impact on key metrics (for example, engagement, time saved, satisfaction, or retention), ideally across more than one product area.
Hands-on experience with large language models (LLMs) in real applications, including prompt and tool design, retrieval-style patterns (such as RAG), and evaluation and monitoring in production.
Strong computer science fundamentals (e.g., data structures, algorithms, and systems design) and a generalist mindset, comfortable moving between backend, data, and UX to get the job done.
Experience designing, developing, and deploying ML/LLM/AI pipelines in production environments, including experience with model serving, feature engineering, and MLOps practices
Expert-level understanding of distributed data processing technologies and their internals (e.g., Spark execution model, query optimization in Redshift/BigQuery/Snowflake, storage formats like Parquet/ORC)
Proven track record of independently architecting scalable data solutions, from requirements gathering and technical design through implementation and cost optimization, with focus on long-term maintainability and ROI

Preferred Experience

Proven track record of technical leadership, including mentoring senior engineers, driving engineering standards and best practices, and influencing data platform strategy across the organization
Hands-on experience architecting federated query engines (DuckDB, Trino, Presto, Starburst) over lakehouse platforms, including catalog integration (Glue, Iceberg, Hudi), query optimization strategies, and cost-effective compute scaling patterns
Deep expertise building orchestration platforms with Airflow (or similar), including custom operators, dynamic DAG generation, and framework-level optimizations for complex dependency management
Advanced experience with serverless and event-driven architectures, including designing systems that leverage AWS Lambda, Step Functions, EventBridge, or Databricks workflows for cost-efficient, auto-scaling data processing
Experience building customer-facing embedded analytics solutions (Cube.js, Metabase, Superset, or similar) with complex data modeling, access control, and performance optimization

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Edtech.com's Summary

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