About this role.
Instructure seeks a Senior Data Engineer to build and scale core data infrastructure and authoritative data marts. The role centers on developing ETL/ELT pipelines into Snowflake, processing high-volume workloads with Databricks and PySpark, and managing supporting AWS data services. The engineer will use dbt to create tested, version-controlled models and help implement a semantic layer that standardizes business metrics. Success requires strong data governance, automated quality monitoring, root-cause analysis, and cross-functional collaboration around trusted data definitions.
Role DNA
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Core skills
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Cover letter sample
Dear Hiring Team,
I am excited to apply for the Senior Data Engineer role at Instructure. My experience building scalable ETL/ELT platforms with Snowflake, dbt, Python, Spark, and AWS aligns strongly with your need for governed, trusted data products.
I have designed modular data models, implemented automated quality controls, and partnered with business stakeholders to establish consistent metric definitions and reduce data discrepancies. I would welcome the opportunity to help Instructure scale its semantic layer and build reliable data marts for downstream teams.
Thank you for your consideration. I look forward to discussing how I can contribute to a high-performing, well-governed data environment.
Sample interview questions
I would start by profiling sources, defining data contracts, and separating ingestion, staging, intermediate, and mart layers. In dbt, I would create reusable, version-controlled models with tests for uniqueness, referential integrity, freshness, and accepted values, then expose curated marts through documented semantic metrics.
I would define each metric centrally with clear grain, dimensions, filters, owners, and source models. I would use dbt Semantic Layer or MetricFlow to expose the definitions consistently, validate outputs against agreed business examples, and establish change-control and documentation practices to prevent metric drift.
I would optimize Snowflake through appropriate warehouse sizing, query profiling, incremental models, partition-aware processing, and careful use of clustering where it materially improves pruning. I would also monitor warehouse utilization, failed jobs, data freshness, and cost trends, then tune workload scheduling and transformation logic based on observed bottlenecks.
For high-volume transformations, I would use Spark with efficient partitioning, predicate pushdown, incremental processing, and avoidance of unnecessary shuffles. I would build idempotent jobs, instrument them with data-quality checks and operational metrics, and use orchestration with retries and alerting so failures are visible and recoverable.
I would investigate from the consumer-facing metric back through the semantic definition, dbt model lineage, source data, and pipeline logs. After identifying the root cause, I would correct the model or ingestion logic, add a regression test or monitoring rule, document the incident, and communicate the impact and remediation to stakeholders.
At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that’s where you come in:
We are looking for a Senior Data Engineer to lead the development and scaling of our core data infrastructure. You won’t just move data; you will be a key contributor in architecting and maintaining our Sources of Truth. Your mission is to transform raw, source data into authoritative, governed data marts by building high-performance pipelines and a robust Semantic Layer that ensures consistency across the entire business.
If you enjoy the challenge of orchestrating complex workflows utilizing Databricks, Fivetran, dbt, and Snowflake—and you take pride in ensuring key metrics are defined once and trusted everywhere—this is the role for you.
What You’ll Do
End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud.
Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency.
High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex data transformations and high-volume workloads.
Implement the Semantic Layer: Collaborate with the team to define, implement, and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers.
Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.
Data Governance & Quality: Implement automated testing and monitoring to ensure the integrity and reliability of finished data marts.
Your Technical Toolkit
Data Warehousing: Expert-level proficiency in Snowflake (clustering, Snowpipe, streams, and tasks) or similar cloud data warehouses.
Analytics Engineering: Advanced mastery of dbt and complex SQL transformation logic, with specific experience building semantic models and metric definitions.
Big Data & Code: Strong Python skills and hands-on experience with Databricks for Spark-based orchestration.
Cloud Infrastructure: Practical experience managing data workloads within AWS.
Version Control: Deep understanding of Git-based workflows and CI/CD for data.
What you’ll need to know/have:
A Data Quality Champion: You believe data is a liability until it’s governed, and you have a passion for data modeling and reducing “metric drift” across the organization.
Scale-Oriented & Efficient: You design for the long term, building streamlined, cohesive data environments that eliminate redundancy and fragmentation. By focusing on modular design and automation, you ensure our infrastructure is stable and easy to navigate as data complexity and volume grow.
A Problem Solver: You find the root cause of data discrepancies and build automated solutions to prevent them from recurring.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here’s a general idea of what you can expect:
Competitive compensation, plus all full-time employees participate in our ownership program – because everyone should have a stake in our success.
Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
Comprehensive wellness programs and mental health support
Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
The technology and tools you need to do your best work
Motivosity employee recognition program
A culture rooted in inclusivity, support, and meaningful connection
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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