Interested in this Data Engineer role at ClassLink, Inc.?
Apply Now →Skills & Technologies
About This Role
Description:
At ClassLink, we believe learning should be accessible, engaging, and empowering for every student, every educator, everywhere. That’s why we build secure, intuitive tools that empower more than 26 million students and educators worldwide. From simplifying access with single sign-on, to advancing equity with usage analytics, to automating secure rostering, our platform helps schools focus on what matters most: teaching and learning.
We’re not just building tools, we’re building what’s next in education. By removing barriers and enabling data-driven decisions, our solutions help educators lead with confidence and students thrive in and out of the classroom every day.
Our team is driven by purpose and grounded in three core values:
- Strive to Grow - always learning, improving, and innovating.
- Deliver Worthwhile Experiences - for our users, our partners, and each other.
- Contribute with Heart - putting people first in everything we do.
These values shape how we work, innovate, and support the communities we serve. If you’re ready to do work that makes a real difference, and grow alongside a team that values collaboration, support, and continuous improvement. ClassLink is the place for you.
The Agentic Data Engineer, IC2 designs, builds, and maintains scalable data platforms, pipelines, integrations, APIs, and cloud-native services supporting ClassLink’s products, analytics, and AI-powered applications. Working across engineering teams, this role develops secure, reliable data solutions and contributes to automation, data quality, governance, reusable frameworks, and engineering best practices. Success requires strong data engineering and cloud expertise, analytical problem-solving, collaboration, and a continuous improvement mindset.
Key Responsibilities
AI-Enabled Data engineering & Technical Excellence
- Design, develop, and optimize scalable data pipelines, REST APIs, and cloud-native data services that support ClassLink products and internal platforms.
- Apply AI-assisted engineering practices, modern data engineering methodologies, and established coding standards to improve engineering effectiveness, solution quality, and developer productivity.
- Ensure data engineering solutions comply with established standards for security, testing, observability, data quality, and software reliability.
Accountability & Results
- Own technical implementation for assigned data engineering initiatives from design through deployment.
- Design and enhance multi-tenant data architectures that provide secure, scalable, and reliable customer data processing.
- Develop and extend ETL/ELT pipelines by integrating new vendor data sources through REST APIs, SFTP, event-driven integrations, message queues, and other supported data exchange methods.
- Design scalable REST APIs and database solutions that support application functionality, reporting, and downstream integrations.
- Extend relational data models, implement schema migrations, and optimize database performance while maintaining data integrity.
Operational Excellence & Continuous Improvement
- Improve engineering efficiency through automation, workflow optimization, testing improvements, and AI-assisted development practices.
- Develop and maintain automated testing for APIs, ETL processes, and data validation to improve release confidence and data quality.
- Contribute reusable components, technical documentation, engineering standards, and operational runbooks that improve consistency across teams.
- Support and optimize AWS-based data platforms through automation, monitoring, observability, performance tuning, and cost optimization while identifying opportunities to improve scalability, reliability, and operational efficiency.
Collaboration & Communication
- Collaborate with Product Management, Agentic Engineering, Cloud Engineering, Security Engineering, Quality Engineering, Architecture, Infrastructure, Analytics, and fellow Agentic Data Engineers to deliver reliable, scalable, and secure data solutions.
- Participate in technical design discussions, code reviews, architecture reviews, and engineering working groups.
- Share technical knowledge and mentor Associate Data Engineers through collaborative problem-solving, design guidance, and code reviews.
This role is remote with quarterly in-person strategy meetings.
NOTE: This job description is not intended to be all-inclusive. Employees may be asked to perform other related duties as assigned to meet the ongoing needs of the organization.
Requirements: Required Skills:
- Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Technology, or a related field, or equivalent combination of education and experience.
- 3–5 years of experience in data engineering, backend software engineering, or cloud-based data platform development.
- Advanced proficiency in Python, including object-oriented programming, type hinting, decorators, context managers, and asynchronous programming concepts.
- Strong experience with SQLAlchemy ORM, Alembic migrations, relational database design, and PostgreSQL.
- Experience designing, developing, testing, and supporting REST APIs using FastAPI or similar frameworks.
- Experience designing and supporting ETL/ELT pipelines and integrating data from APIs, SFTP, vendor systems, and other external sources.
- Experience designing and supporting AWS cloud services and cloud-native data solutions, including storage, compute, networking, monitoring, and serverless capabilities.
- Experience with Git, CI/CD pipelines, automated testing, observability, and Agile software development.
- Strong analytical, troubleshooting, and problem-solving skills with the ability to independently resolve complex technical issues.
- Excellent communication and collaboration skills with experience working on cross-functional Agile teams.
- Experience using AI-assisted software development tools and modern engineering practices to improve developer productivity and software quality.
Preferred:
- Experience designing and supporting multi-tenant SaaS platforms.
- Experience with Snowflake or other cloud data warehouse technologies.
- Experience with data orchestration platforms and workflow automation tools.
- Experience building scalable event-driven or distributed data processing systems.
- Experience designing or supporting vector databases, Retrieval-Augmented Generation (RAG) pipelines, semantic search, feature stores, or other AI-ready data architectures.
- Familiarity with Infrastructure as Code, containerization, Kubernetes, or serverless architectures.
- Experience mentoring junior engineers or leading technical initiatives.
- Knowledge of data governance, data quality frameworks, observability, and monitoring practices.
- Experience using AI-assisted development tools to improve software engineering productivity.
- AWS certifications or data engineering certifications.
This role is remote with quarterly in-person strategy meetings.
Benefits:
- Generous Paid Time Off: 4+ weeks of vacation and 13+ paid holidays
- Parental Leave: 12 weeks fully paid for all parents
- Retirement: 401(k) with 0.5:1 company match
- Comprehensive Health Coverage: Medical, dental, and vision plans
- Insurance: Company-paid life, short-term disability, and long-term disability, plus voluntary options including supplemental life, accident, and pet insurance
Perks:
- Volunteer Time Off: Give back through ClassLink Cares paid volunteer days
- Lifelong Learning: Tuition reimbursement for continued education
- Growth & Development: Coaching and internal programs to support career and personal growth
- Supportive Culture: A workplace that values curiosity, compassion, and continuous learning
- Connection & Community: Annual company retreats and team events with bright, fun coworkers
At ClassLink, we believe the best ideas come from bringing together diverse perspectives. We are committed to building a team that reflects the diversity of the communities we serve and fostering a workplace where every team member feels valued, respected, and empowered to grow.
ClassLink is proud to be an equal opportunity employer. We welcome candidates of all identities, backgrounds, and experiences, and we do not discriminate on the basis of race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other legally protected characteristic.
ClassLink is powered by passionate people dedicated to improving learning in classrooms everywhere. We do this by creating and delivering great products and having fun along the way. Learn more about us at www.classlink.com.
Salary Context
This $105K-$115K range is in the lower quartile 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 ClassLink, Inc., 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 ($110K) sits 38% below the category median. Disclosed range: $105K to $115K.
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.
ClassLink, Inc. AI Hiring
ClassLink, Inc. has 1 open AI role right now. They're hiring across Data Engineer. Based in US. Compensation range: $115K - $115K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.