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Staff Engineer - Real-Time Merchant Analytics
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
What you’ll work on
Full postingArchitect, build and refine high-performance streaming infrastructure for merchant analytics
Build tools and frameworks to improve platform efficiency, scalability and developer experience
Integrate streaming systems with Shopify's broader data ecosystem
Influence the technical roadmap for merchant analytics with product and data teams
Mentor and uplevel engineers on the team
From the employer’s posting
Architect, build, and refine high-performance streaming infrastructure tailored to large-scale, real-time merchant analytics.
Develop tools and frameworks to boost platform efficiency, scalability, and developer experience across the team.
Collaborate with cross-functional teams to integrate streaming systems with Shopify's broader data ecosystem.
Partner with product and data teams to influence the technical roadmap and shape the future of merchant analytics.
Mentor and uplevel engineers on the team, fostering an environment of innovation and technical excellence.
What you’ll bring
All qualificationsCore experience
- Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming).
- Proficiency in multiple programming languages
- Experience with analytical databases like ClickHouse or BigQuery.
- Strong understanding of containerization (Docker, Kubernetes).
- Deep expertise in handling distributed systems challenges: late-arriving data, exactly-once semantics, backfill strategies, and data consistency.
Preferred experience
- Java, SQL required; Python, Ruby a plus
Qualification wording
Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming).
Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus).
Experience with analytical databases like ClickHouse or BigQuery.
Strong understanding of containerization (Docker, Kubernetes).
Deep expertise in handling distributed systems challenges: late-arriving data, exactly-once semantics, backfill strategies, and data consistency.
Posting record
Report an errorActively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 29, 20266 days ago
- Recorded sightings
- 6
- Last seen by us
- Nov 3, 2026
- Employer says posted
- Oct 23, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Shopify is hiring
20 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Java
- Python
- Ruby
- SQL
- BigQuery
- ClickHouse
- Docker
- Kafka
- Kubernetes
- Spark
Where the posting names them
What Makes This Exciting? You'll work across multiple languages and technologies – Java, Ruby, Python, SQL, Flink, and ClickHouse – choosing the right tool for each challenge, model data elegantly, and turning data pipeline development into a configuration exercise rather than a coding marathon. You'll tackle fascinating problems: How do you architect lightning-fast real-time modeling that seamlessly combines data from multiple tables? How do you handle late-arriving data in distributed streams? What's the most elegant approach to backfill terabytes while maintaining real-time processing?
- Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming). - Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus). - Experience with analytical databases like ClickHouse or BigQuery.
- Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus). - Experience with analytical databases like ClickHouse or BigQuery. - Strong understanding of containerization (Docker, Kubernetes).
- Experience with analytical databases like ClickHouse or BigQuery. - Strong understanding of containerization (Docker, Kubernetes). - Deep expertise in handling distributed systems challenges: late-arriving data, exactly-once semantics, backfill strategies, and data consistency.
- Extensive experience in data infrastructure engineering, particularly in building and scaling real-time data platforms. - Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming). - Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus).
Your next step
Complete your application on www.shopify.com. The employer’s form will show what is required.
Already applied? Track this application
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
We're building the future of Real-Time Merchant Analytics at Shopify!
As a Staff Engineer you'll be at the forefront of reimagining how merchant data flows through modern streaming architectures. This isn't your typical infrastructure role – you'll be crafting solutions that challenge conventional approaches to data processing at global scale.
What Makes This Exciting?
You'll work across multiple languages and technologies – Java, Ruby, Python, SQL, Flink, and ClickHouse – choosing the right tool for each challenge, model data elegantly, and turning data pipeline development into a configuration exercise rather than a coding marathon.
You'll tackle fascinating problems: How do you architect lightning-fast real-time modeling that seamlessly combines data from multiple tables? How do you handle late-arriving data in distributed streams? What's the most elegant approach to backfill terabytes while maintaining real-time processing?
We embrace AI and LLMs to accelerate repetitive tasks, freeing you to focus on the creative problem-solving that makes this work truly rewarding.
If you love turning "impossible" requirements into beautiful solutions, this is your playground.
What You'll Do
Architect, build, and refine high-performance streaming infrastructure tailored to large-scale, real-time merchant analytics.
Develop tools and frameworks to boost platform efficiency, scalability, and developer experience across the team.
Collaborate with cross-functional teams to integrate streaming systems with Shopify's broader data ecosystem.
Partner with product and data teams to influence the technical roadmap and shape the future of merchant analytics.
Mentor and uplevel engineers on the team, fostering an environment of innovation and technical excellence.
What You'll Need
Extensive experience in data infrastructure engineering, particularly in building and scaling real-time data platforms.
Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming).
Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus).
Experience with analytical databases like ClickHouse or BigQuery.
Strong understanding of containerization (Docker, Kubernetes).
Deep expertise in handling distributed systems challenges: late-arriving data, exactly-once semantics, backfill strategies, and data consistency.
Outstanding problem-solving skills with a focus on complex technical challenges at scale.
A collaborative mindset and the ability to thrive in a diverse, dynamic team environment.
Source details
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- Pay
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- Location & working pattern
Americas
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