About this role.
Instacart is seeking an Analytics Engineer II to build and maintain marketing analytics data models and ETL pipelines supporting paid marketing, SEO, retailer marketing, and attribution. The role requires end-to-end ownership of dimensional models, data quality testing, documentation, monitoring, and pipeline observability using dbt, Snowflake, Airflow, and SQL. This engineer will partner closely with Data Scientists, Analysts, and Marketing stakeholders to translate business questions into trusted datasets. The position is remote but limited to candidates residing in Ontario, Alberta, British Columbia, or Nova Scotia. It is a mid-level individual-contributor role with meaningful technical ownership and cross-functional impact on marketing-spend decisions.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader CA market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Sample interview questions
I would start by defining the business grain, such as campaign-day-channel or conversion-event, and align metric definitions with marketing stakeholders. I would create conformed dimensions for date, channel, campaign, retailer, geography, and attribution model, with fact tables for spend, impressions, clicks, conversions, and attributed revenue. I would document lineage, apply incremental loading where appropriate, and include tests for unique keys, referential integrity, freshness, and reconciliation to source platforms.
I would first identify the affected time window, channels, and downstream models, then check pipeline freshness, orchestration logs, source extract volumes, and recent code or schema changes. I would compare raw-source counts with staging and mart-layer counts to isolate where the discrepancy began. After correcting the root cause, I would backfill affected data, validate results with stakeholders, and add monitoring or tests to prevent recurrence.
I clarify the decision being supported, the required level of accuracy, and whether the request is recurring. For urgent one-time needs, I can provide a clearly labeled exploratory solution while documenting assumptions and limitations. When a request is likely to recur or affect important decisions, I prioritize a reusable modeled dataset, tests, documentation, and an agreed ownership model.
I would use layered modeling conventions, source freshness checks, schema tests, custom business-rule tests, and clear model documentation. Pull requests should receive peer review, and significant changes should have lightweight design proposals covering grain, dependencies, metric definitions, and migration risks. I would also monitor job failures, data volume anomalies, and key KPI changes to catch issues not covered by static tests.
I would begin by agreeing on the experiment or attribution methodology, required input grain, treatment definitions, outcomes, and exclusions. I would produce versioned, reproducible feature and outcome datasets with transparent transformation logic and validation against source systems. Throughout the project, I would communicate assumptions, data limitations, and changes early so analytical conclusions remain interpretable and repeatable.
We’re transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work.
Overview
The Marketing Enablement & Technology (MET) team sits natively within Instacart’s Marketing organization, owning the data foundations that power Paid Marketing, SEO, and Retailer Marketing attribution. These datasets directly inform how we allocate hundreds of millions of dollars in marketing spend and how we measure growth.
We’re hiring an Analytics Engineer II to help build and evolve the marketing analytics data foundation. In this role, you’ll independently design and deliver high-quality, business-aware data models and pipelines that Data Scientists and Analysts trust — owning your work end-to-end, raising the quality bar through code reviews and design discussion, and growing toward broader technical ownership of marketing data.
About the Job
- Marketing Analytics Data Development: Independently build and maintain high-quality dimensional data models and ETL pipelines that support marketing analytics across Paid Marketing, SEO, Retailer Marketing, and attribution — delivering complete data assets end-to-end with minimal oversight.
- Cross-functional Partnership: Work closely with Data Scientists, Analysts, and Marketing stakeholders to translate analytical needs and business questions into data requirements, and deliver trusted, decision-ready datasets.
- Data Quality Ownership: Own data quality for the models you build — writing tests, documentation, and monitoring, and resolving data issues at their root cause across the marketing workflows you support.
- Code Review & Design: Conduct thorough code reviews, write and share designs publicly before building, and apply simple, reusable modeling patterns that leave the codebase better than you found it.
- Pipeline Evolution & Observability: Improve existing marketing data pipelines to reduce manual effort and improve performance, enhance observability (logging, metrics, freshness and quality checks), and participate in incident response for the data platform.
About You
Minimum Qualifications
- 3–5 years of experience in Analytics Engineering, Data Engineering, or closely related roles, with hands-on ownership of production data models and pipelines.
- Strong SQL skills and hands-on experience building well-architected dimensional data models (star schemas, fact/dimension tables, SCDs).
- Hands-on experience with the modern data stack, including dbt, Snowflake, and Airflow.
- Working knowledge of marketing data and metrics — including paid media performance, attribution concepts, and channel-level measurement — or the ability to ramp quickly.
- Demonstrated ability to deliver complete data models and pipelines independently, manage multiple priorities, and write clean, well-documented, well-tested code.
- Effective communicator who can collaborate across data, marketing, and engineering teams to understand requirements and deliver solutions.
Preferred Qualifications
- Experience partnering with Data Scientists on experimentation, attribution, or incrementality measurement.
- Familiarity with marketing platforms (ie Google Ads, Meta, Google Analytics, SEO tooling).
- Python experience for automation or advanced transformations.
- Comfort using AI-assisted developer tools to improve code quality and speed (e.g., generating tests, validating logic, comparing suggestions).
- Experience contributing to analytics engineering best practices such as testing, documentation, or data quality standards.
#LI-Remote
Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For Canadian based candidates, the base pay ranges for a successful candidate are listed below.
CAN
$139,000—$146,500 CAD
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