Senior Analytics Engineer
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Compensation
Salary & market context
Salary not listed
- Nice to have: Sales and Marketing domain experience Past experience collaborating closely with data scientists, sales, and marketing managers Benefits: Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Comprehensive Medical Benefits & Health Care Spending Account Registered Retirement Savings Plan with matching contributions Income Replacement Programs Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-REMOTE In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI).
Requirements
Top requirements
- s:
- Advanced degree in a quantitative discipline such as statistics, operations research, computer science, applied mathematics, economics, or physics
- For M.S. holders: 5+ years of industry experience working with large-scale ETL systems (implementation, strategy, and maintenance), building clean, maintainable, code and systems (Python preferred) in a production environment.
- For Ph.D. holders: 4+ years of industry experience working with large-scale ETL systems (implementation, strategy, and maintenance), building clean, maintainable, code and systems (Python preferred) in a production environment.
Perks & setup
Benefits candidates care about
- s:
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
Why candidates care
Benefits & perks
- s:
- Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
- Family Planning Support
- Gender-Affirming Care
- Mental Health & Coaching Benefits
- Comprehensive Medical Benefits & Health Care Spending Account
- Registered Retirement Savings Plan with matching contributions
- Income Replacement Programs
Start here
Requirements
- s:
- Advanced degree in a quantitative discipline such as statistics, operations research, computer science, applied mathematics, economics, or physics
- For M.S. holders: 5+ years of industry experience working with large-scale ETL systems (implementation, strategy, and maintenance), building clean, maintainable, code and systems (Python preferred) in a production environment.
- For Ph.D. holders: 4+ years of industry experience working with large-scale ETL systems (implementation, strategy, and maintenance), building clean, maintainable, code and systems (Python preferred) in a production environment.
- Experience working in Sales and Marketing domains.
- Strong programming proficiency in Python, SQL, Spark, Scala, etc.
- Experience with data modeling, ETL and ELT concepts, and patterns for efficient data governance. Experience with manipulating massive-scale structured and unstructured data.
- Experience with data workflows (such as Airflow), data modeling, front-end or back-end engineering.
Responsibilities
What you'll do
- s:
- Be the Analytics Engineering lead within the Sales and Marketing organization and a key contributor to the success of Data Science data quality, performance, and automation initiatives.
- Be the data steward for Sales and Marketing: architect and improve the collection of underlying data while also creating ETLs, reporting dashboards, data aggregations and other deliverables needed for business tracking, advertiser outreach and acquisition, marketing campaigns, and other data-driven activities.
- Develop and maintain robust data pipelines and workflows for data ingestion, processing, and transformation. Work closely with engineering to ensure the quality and reliability of these data pipelines.
- Create user-friendly tools and applications for internal use across Data Science and cross-functional teams, streamlining data analysis and reporting processes. Drive widespread adoption of these tools and applications with a relentless focus on automation, consistency, and reliability.
- Lead transformational efforts to build a data-driven culture at Reddit by enabling data self-service.
- Provide technical guidance, mentorship, coaching and/or training to data scientists and other technical partners.
- Serve as a thought partner for data scientists, engineering managers, and leadership on data foundations, communicating and shaping the data foundations roadmap and strategy for Reddit.
Role snapshot
About the role
Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .
Reddit has a flexible workforce! If you happen to live close to one of our physical office locations, our doors are open so you can come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence
We are looking for a talented and driven individual to be a key part of our Analytics Engineering team within the Data Science organization, focused on the Sales and Marketing domain. We are looking for someone who can work closely with Data Scientists and members of Sales and Marketing cross-functional teams to curate, develop, and deploy the right data and analytic tooling to drive Reddit’s business forward and provide a data and tooling foundation that will last decades. Your work will empower thousands of your colleagues to grow our Sales and Marketing reach.
Successful candidates have a strong track record of understanding and deeply caring about the purpose of data to support business goals, and can act as an effective conduit between Data Producers and Data Consumers. This role sits at the intersection of Data Science and Data Engineering, and the ideal candidate has skills, experience, and passion in both areas.
More detail
Nice to have
- e:
- Sales and Marketing domain experience
- Past experience collaborating closely with data scientists, sales, and marketing managers
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