Remote - Canada
Open 3 months, first seen by us 96 days agoListed location: Remote - CanadaRead the full posting
Employment type in the employer’s posting: Full timeFrom the employer’s posting
Design, develop, and maintain real-time and batch data pipelines
Build AI-powered tools that let teammates query data in natural language
Design and optimize data models using Star Schema and Data Vault
Translate business requirements into analytics-ready datasets
Mentor teammates and drive data engineering best practices
Design, develop, and maintain real-time and batch data pipelines using technologies such as Kafka, Databricks, and Snowflake.
Build AI-powered data experiences that enable teammates to explore and analyze data using natural language
Design and optimize modern data models, including Star Schema and Data Vault methodologies
translate complex business requirements into scalable, analytics-ready datasets
while mentoring teammates and contributing to the continued growth of the Data Engineering Team
Open 3 monthsWe first saw this posting 96 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 96
Up longer than most. Similar jobs usually come down between day 6 and day 54.
Well past DraftKings’s usual: its jobs come down by about day 33.
From 11,032 similar postings that came down, and 16 of DraftKings’s. Coming down is not always a hire.
6 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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.
- At least 3 years of experience designing, building, and maintaining scalable data platforms and distributed data systems. - Strong proficiency in Python and SQL, with hands-on experience building real-time data streaming solutions. - Experience working with Snowflake, Databricks, Kafka, and modern cloud-based data platforms.
- Develop reliable data solutions that power business-critical reporting, analytics, and platform insights. - Build AI-powered data experiences that enable teammates to explore and analyze data using natural language, extending beyond text-to-SQL to support intelligent, autonomous workflows. - Partner with Engineering, Analytics, and cross-functional stakeholders to translate complex business requirements into scalable, analytics-ready datasets.
- Experience working with Snowflake, Databricks, Kafka, and modern cloud-based data platforms. - Experience with cloud infrastructure, preferably Amazon Web Services (AWS), and familiarity with infrastructure and DevOps tools such as Terraform, PagerDuty, and Datadog. - Deep understanding of data warehousing, data lakes, ETL frameworks, and modern data modeling techniques, including Star Schema and Data Vault.
- Build scalable, high-performance data infrastructure that transforms raw and historical data into trusted, actionable insights. - Design, develop, and maintain real-time and batch data pipelines using technologies such as Kafka, Databricks, and Snowflake. - Develop reliable data solutions that power business-critical reporting, analytics, and platform insights.
- Strong proficiency in Python and SQL, with hands-on experience building real-time data streaming solutions. - Experience working with Snowflake, Databricks, Kafka, and modern cloud-based data platforms. - Experience with cloud infrastructure, preferably Amazon Web Services (AWS), and familiarity with infrastructure and DevOps tools such as Terraform, PagerDuty, and Datadog.
- Deep understanding of data warehousing, data lakes, ETL frameworks, and modern data modeling techniques, including Star Schema and Data Vault. - Experience working with NoSQL databases such as MongoDB or DynamoDB; familiarity with Sigma or similar business intelligence tools is a plus. - Hands-on experience using AI-assisted engineering tools such as Claude Code, Codex, or similar technologies to build agents, automate workflows, and accelerate software development.
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Yes. We’ve followed one other Senior Data Engineer job there since July 2026. It came down after 92 days.
New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
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.
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.
At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It’s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We’re not waiting for the future to arrive. We’re shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together.
As a Senior Data Engineer, you'll build the real-time data capabilities that power analytics, reporting, and intelligent experiences across our platform. You'll design and scale modern data infrastructure, turning complex data into trusted insights that help teams make faster, more informed decisions. Working across Engineering, Analytics, and business teams, you'll solve challenging technical problems, shape the future of our data architecture, and create the foundation for AI-powered experiences that accelerate how we build and innovate.
Build scalable, high-performance data infrastructure that transforms raw and historical data into trusted, actionable insights.
Design, develop, and maintain real-time and batch data pipelines using technologies such as Kafka, Databricks, and Snowflake.
Develop reliable data solutions that power business-critical reporting, analytics, and platform insights.
Build AI-powered data experiences that enable teammates to explore and analyze data using natural language, extending beyond text-to-SQL to support intelligent, autonomous workflows.
Partner with Engineering, Analytics, and cross-functional stakeholders to translate complex business requirements into scalable, analytics-ready datasets.
Design and optimize modern data models, including Star Schema and Data Vault methodologies, while championing data quality, governance, and observability.
Drive best practices for data engineering, streaming, transformation, and platform performance while mentoring teammates and contributing to the continued growth of the Data Engineering Team.
Collaborate in an agile environment, delivering scalable solutions that support evolving business and platform needs.
A Bachelor's Degree in Computer Science or a related field, or an equivalent combination of education, training, and professional experience.
At least 3 years of experience designing, building, and maintaining scalable data platforms and distributed data systems.
Strong proficiency in Python and SQL, with hands-on experience building real-time data streaming solutions.
Experience working with Snowflake, Databricks, Kafka, and modern cloud-based data platforms.
Experience with cloud infrastructure, preferably Amazon Web Services (AWS), and familiarity with infrastructure and DevOps tools such as Terraform, PagerDuty, and Datadog.
Deep understanding of data warehousing, data lakes, ETL frameworks, and modern data modeling techniques, including Star Schema and Data Vault.
Experience working with NoSQL databases such as MongoDB or DynamoDB; familiarity with Sigma or similar business intelligence tools is a plus.
Hands-on experience using AI-assisted engineering tools such as Claude Code, Codex, or similar technologies to build agents, automate workflows, and accelerate software development.
Strong communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional environment while mentoring and supporting other engineers.
We’re a publicly traded (NASDAQ: DKNG) technology company headquartered in Boston. As a regulated gaming company, you may be required to obtain a gaming license issued by the appropriate state agency as a condition of employment. Don’t worry, we’ll guide you through the process if this is relevant to your role.
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Remote - Canada
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