AmazonPosted 4mo ago

Data Engineer, Alexa Smart Home

Data Engineer, Alexa Smart Home at Amazon scores 76 out of 100 on AI centrality, which makes it AI Level 3 of 4 (Works on AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

bedrockreplicatesagemaker

prompt-engineeringfine-tuning

The Alexa Smart Home Team is looking for a talented, highly motivated Data Engineer to join our Decision Sciences team. We provide actionable business insights that inform future products and services that will power the next generation of Alexa-enabled smart home devices. As a Data Engineer, you will work in one of the world's largest and most complex data-warehouse environments. You will work closely with Product Management, Software Development, Data Science, and other peer Data Engineering teams to develop scalable and innovative analytical solutions, process and store terabytes of low latency structured and unstructured data, and enable the Smart Home team to build successful, data driven strategies to provide the best customer experience. Data privacy is the top priority, and you will have the opportunity to design data infrastructure that delivers the best-in-class data privacy and compliance for retaining the customer trust.

You will be responsible for designing and implementing a data integration and analytical environment using various tools and using Python, Scala, or Java to automate the ETL, analytics, and data quality platform from the ground up. You will design and implement complex data models, model metadata, build reports and dashboards, and own data presentation and dashboarding tools for the end users of our data products and systems. You will write scalable, highly tuned SQL queries running over billions of rows of data and will develop data access platforms to drive adoption of data driven decision making across the Smart Home organization. Beyond traditional data engineering, you will leverage AI capabilities to enhance data pipeline development, transforming raw data into structured datasets ready for downstream AI model consumption, analytics use cases, and proactive data quality validations. This agility is critical at a moment when speed of AI adoption directly translates to business impact for our smart home customers.

You should have deep expertise in the design, creation, management, and business use of large datasets, across a variety of data platforms. You should have excellent business and interpersonal skills to be able to work with business owners to understand data requirements, and to implement efficient and scalable ETL solutions. You should be an authority at crafting, implementing, and operating reliable, scalable, cost optimized solutions to replicate and transform data from production systems into offline datastores.

Key job responsibilities
• Work with the product and development teams within Alexa org to understand the product vision and requirements.
• Work with Product Managers, BI engineers, Software Engineers and Data Scientists to design, implement and support high quality data products.
• Partner with cross functional teams across Devices organization to ingest relevant datasets into the Alexa Smart Home BI data-warehouse.
• Collaborate with other peer data engineers to build self-service data platforms that possess key capabilities like data discovery, data lineage, proactive data monitoring and security/compliance monitoring.
• Manage data infrastructure, including capacity planning, cost optimization, and performance tuning.
• Leverage and manage AWS services like Bedrock, Sagemaker, S3, Redshift, Athena, Kinesis, Lambda, Data Lake etc.
• Implement data pipelines using best practices in data modeling, ETL/ELT processes by leveraging AWS technologies and big data tools.
• Build data pipelines that support AI/ML use cases and enable integration with AWS AI services such as Amazon Bedrock and SageMaker to embed AI capabilities into production workflows.
• Collaborate with Data Scientists to adopt best practices in data system creation, data integrity, test design, analysis, validation, and documentation.
• Help continually improve ongoing data infrastructure processes, automating or simplifying self-service modeling and production support for stakeholders.

About the team
The Alexa Smart Home Decision Sciences team provides the data and analytical foundation that powers insights across our rapidly growing smart home ecosystem. We work with data from millions of connected devices, customer interactions, and partner integrations to help shape product strategy, improve customer experience, and drive business growth. We operate in a collaborative environment where innovation and customer obsession are at the core of everything we do.

Basic qualifications

- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- 4+ years of SQL experience

Preferred qualifications

- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with Apache Spark / Elastic Map Reduce
- Experience working with large language models (LLMs), including prompt engineering, model selection, and instructions fine-tuning to optimize model performance for analysis against large datasets
- Experience with scripting and API integration with AWS AI services such as Amazon Bedrock and SageMaker

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.

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Skills and AI tools this role asks for

Prompt EngineeringFine TuningBedrockReplicateSagemaker

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What are the limits of Bedrock that you've run into, and how did you work around them?
What's a project where you used Replicate hands-on?
Walk me through how you've used Sagemaker in your day-to-day work.

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