ADCI
Data Science, Data Engineering, selling partner services
SeniorDataEngineer,WWFBACentralAnalytics
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optimal for Senior candidates.
“Senior Data Engineer, WW FBA Central Analytics at ADCI. Skills: Data Engineering, Data Lakehouse, AI/ML, GenAI. Architect Data Lakehouse. Implement Data Lakehouse”
What You'll Achieve.
Deliver high-quality results; Transform data into insights; Deliver proactive insights; Improve query performance
Industry & Context.
Solving hard problems
What They're Looking For.
Must Have
7+ years of data engineering experience, 5+ years of data warehouse technical architectures, 5+ years of data modeling, 5+ years of infrastructure components, 5+ years of ETL/ELT, 5+ years of reporting/analytic tools, 5+ years of data structures, 5+ years of hands-on SQL coding, Experience with SQL, Experience mentoring team members, Experience with AWS technologies, Experience with Redshift, Experience with S3, Experience with AWS Glue, Experience with EMR, Experience with Kinesis, Experience with FireHose, Experience with Lambda, Experience with IAM roles, Experience with permissions, Experience with programming/scripting, Experience with Batch, Experience with VB, Experience with PowerShell, Experience with Java, Experience with C#, Experience with Chef, Experience with Perl, Experience with Ruby, Experience with PHP, Experience in Bigdata architecture, Experience with analytical skills, Experience with attention to detail, Experience with effective communication, Experience building data flows, Experience maintaining data flows, Experience building data pipelines, Experience maintaining data pipelines, 4+ years of Data & AI experience, 4+ years of AI/ML experience, 4+ years of GenAI experience, 4+ years of Analytics experience, 4+ years of Database experience, 4+ years of Storage experience
Nice to Have
Experience with big data technologies, Experience with Hadoop, Experience with Hive, Experience with Spark, Experience with EMR, Experience operating large data warehouses, Experience training ML systems, Experience deploying ML systems, Experience with data infrastructures, Experience with relational analytic DBMS, Experience with Elastic-Search, Experience with Big Data EMR/EC2/Glue/Lambda, Experience in data mining, Experience with ETL, Experience using databases in business, Experience in machine learning, Experience in data mining, Experience in information retrieval, Experience in statistics, Experience in natural language processing, Experience developing LLMs in production, Experience deploying LLMs in production, Experience building analytic data products, Experience building analytic data solutions, Experience in Redshift, Experience managing firewalls, Experience leading technical initiatives, Experience leading key deliverables, Experience leading large teams, Experience developing high-performing teams, Experience managing high-performing teams
What You'll Do.
Architect Data Lakehouse
Implement Data Lakehouse
Unify structured data
Unify unstructured data
Lead migration from Redshift
Target query performance improvement
Establish metadata management
Automate data classification
Design data ingestion patterns
Enforce data ingestion patterns
Implement quality controls
Implement validation gates
Architect metrics repository
Implement data quality frameworks
Implement staging-first policies
Implement automated validation pipelines
Design metrics schemas
Support analytical queries
Optimize for AI retrieval
Develop intelligent orchestration
Generate metrics workflows
Lead design of semantic models
Balance analytical performance
Implement federated query capabilities
Apply query optimization techniques
Architect vector database infrastructure
Manage large-scale embeddings
Ensure low-latency retrieval
Integrate schema definitions
Enable AI-accessible data contracts
Build monitoring frameworks
Build alerting frameworks
Ensure failure detection
Ensure rapid resolution
Establish schema change management
Establish data quality SLAs
Generate proactive insights
How You'll Work.
Team & Collaboration
Strategic partnerships; Global product teams; Global program teams; Global technology teams
Communication Scope
Effective communication
Process & Methodology
Key deliverables
Full Job Description
Worldwide Fulfillment by Amazon (WW FBA) empowers millions of sellers to scale globally through Amazon's leading fulfillment network. FBA sellers deliver fast, reliable Prime-eligible shipping and hassle-free returns to customers worldwide—enabling them to focus exclusively on business growth while Amazon handles operational logistics. The WW FBA Central Analytics team architects and maintains data infrastructure that delivers critical insights to WW FBA leadership. This team forms strategic partnerships across global product, program, and technology teams to unify datasets, implement self-service analytics platforms, and develop AI capabilities that transform raw data into actionable insights. We are looking for a Senior Data Engineer who thrives on solving hard problems, shaping new capabilities, and delivering high-quality results in a fast-paced environment. You will be at the forefront of integrating LLM-powered solutions with robust backend systems, ensuring they scale securely and reliably to serve global customers. This role sits at the intersection of data engineering and AI - you will own the data foundation that determines whether GenAI-powered insights are trustworthy, fast, and scalable. You will work directly on executive-level initiative to deliver proactive, AI-generated insights across FBA metrics to business leadership worldwide. Key job responsibilities - Architect and implement a scalable, cost-optimized S3-based Data Lakehouse that unifies structured and unstructured data from disparate sources across 8 WW FBA metrics domains. - Lead the strategic migration from Redshift-centric architecture to a flexible lakehouse model, targeting query performance improvement from 60–300 seconds to under 10 seconds. - Establish metadata management with automated data classification and lineage tracking. - Design and enforce standardized data ingestion patterns with built-in quality controls and validation gates. - Architect a centralized metrics repository tha
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