MHLS Business Intelligence Engineer II, Human-AI Enablement and eXperiences
Amazon is hiring an MHLS Business Intelligence Engineer II, Human-AI Enablement and eXperiences in Seattle, United States. It pays $100k-$160k a year and Level rates it ; you can apply on Amazon.
Level's read on this job
- Level rates this role Little AI ●○○○: aI is not part of the work.
- Level classifies it as a mid-level role.
- Level lists 22,000+ other open roles at Amazon.
- The role was posted in the last few days.
AI in this role
As the BIE on the MHLS team, you will design, build, and own the data pipelines and integration layer that bring together contact handling, workforce management, and case management systems into a single, reliable source of truth. You will be the technical owner of data quality — building the validation, monitoring, and reconciliation that ensure every metric the business relies on is accurate. You will model data, engineer ETL pipelines, integrate disparate source systems, and run technical deep dives into data quality and integration issues, tracing a discrepancy back to its root cause so that the insights built on top are never wrong.
Basic qualifications
- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- 1+ years of SQL, ETL or Oracle experience
- 1+ years of processing large, multi-dimensional datasets from multiple sources experience
- 1+ years of performing statistical analysis experience
- 1+ years of developing automated reporting experience
- 3+ years of in the job offered or a related occupation experience
- 1+ years of using SQL, ETL (Extract, Transform, Load), or Oracle experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
Preferred qualifications
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
- Knowledge of and proficiency in the use of Python scripting language
- Experience building data quality and monitoring frameworks
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 99,500.00 - 160,000.00 USD annually
How we rate this
MHLS Business Intelligence Engineer II, Human-AI Enablement and eXperiences at Amazon rates 30 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.
Little AI. AI is not part of the work.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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