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Vetted Data Governance Professionals

Pre-screened and vetted.

Data GovernancePythonSQLAWSCI/CDDocker
NK

Nagarjuna Kanamarlapudi

Director-level Data Platform Engineering Leader specializing in data governance and quality

Sunnyvale, CA15y exp
LinkedInBITS Pilani
Amazon RedshiftApache HadoopApache KafkaApache SparkData EngineeringData Governance+32
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SY

Sen Yuan

Staff Machine Learning Engineer specializing in LLMs and Generative AI

Greater Seattle Area12y exp
MetaNankai University
A/B TestingAnomaly DetectionAWSBigQueryCUDAData Governance+93
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JJ

John Jefferson

Senior Data Engineer specializing in cloud data platforms and real-time streaming

Cape May, NJ13y exp
AmazonNJIT
PythonSQLScalaJavaBashShell Scripting+105
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NS

Neeraj Sharma

Screened

Executive Engineering Leader specializing in AI/ML platforms and cloud-scale distributed systems

Sammamish, WA19y exp
Intalent.aiInstitute of Engineering & Technology

“Senior engineering/technology leader with experience driving board-backed, multi-year platform transformations in retail commerce (American Eagle) and scaling ML/product delivery platforms at Amazon Alexa. Known for data-driven build-vs-buy decisions, org scaling (6→80), and measurable outcomes including 98.5%→99.9% uptime, 234 experiments generating ~$200M revenue lift, and increasing delivery velocity from 1.5 to 30 features/week while transitioning to DNN/large-model architectures.”

AgileAPI IntegrationAWS LambdaBudget ManagementComplianceCross-Functional Leadership+171
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AK

Amit Khanna

Director-level Software Engineering Leader specializing in AI, Data Platforms, and Ads/FinTech

Menlo Park, California22y exp
MetaDr. A.P.J. Abdul Kalam Technical University
A/B TestingAnalyticsAWSAWS LambdaCRMData Analytics+65
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AM

Ayush Minocha

Senior AI/ML Engineering Manager specializing in NLP, computer vision, and MLOps

11y exp
AppleUCLA
PythonSQLJavaScriptBashJavaC+++88
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DJ

DaeJin Jeong

Director of Risk & Control Management specializing in financial services governance and regulatory remediation

New York, NY9y exp
American ExpressWharton School
Data governanceData warehousingPythonRoot cause analysisServiceNowTableau+49
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SR

Srinu Raghu

Executive Engineering Leader specializing in data platforms, AI/ML, and fraud detection

Chicago, IL20y exp
AmazonUniversity of Michigan
.NETAgileAPI DevelopmentAWSBudget ManagementCI/CD+68
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SM

Shiva Mangapuram

Senior AI/ML Engineer specializing in Generative AI, RAG, and MLOps for FinTech

CA5y exp
StripeFlorida Institute of Technology
A/B TestingAgileAmazon BedrockAmazon CloudWatchAmazon ECSAmazon S3+194
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MW

Moied Wahid

Executive CTO/CDAO specializing in cloud-native platforms, data engineering, and AI security

20y exp
Stealth
AnalyticsBatch ProcessingBudget ManagementCI/CDCost OptimizationData Engineering+58
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TD

Thomas Douaihy

Screened ReferencesStrong rec.

Executive CIO/CDO/CTO specializing in digital transformation, data/AI, and cloud modernization

Florida/Missouri34y exp
PurinaWashington University in St. Louis

“Operations/process improvement leader with executive experience at Nestlé Purina, applying Lean/value stream mapping to improve IT service response times and user satisfaction. Led the integration of Merrick Pet Food into Purina, addressing scaling constraints by redesigning outdated processes. Also served as a technology mentor to the Purina executive team/CEO to improve decision-making on technology investments.”

Data GovernanceGenerative AIPrompt EngineeringAutomationDevOpsCloud-Native Architecture+128
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AM

Amit Mathur

Screened ReferencesStrong rec.

Executive Technology Leader specializing in Wealth Management Platforms

New York, NY18y exp
Franklin TempletonBoston University

“Technology leader from Franklin Templeton who modernized wealth management tech by launching client-facing digital web/mobile experiences via a phased data-warehouse-to-real-time strategy, while aligning internal CRM visibility for front office teams. Scaled an engineering org from 15 to ~250 (15 teams) and drove a shift from waterfall to Agile with microservices/micro-frontends and Jira-based transparency to improve delivery velocity.”

Cloud-Native ArchitectureMicrosoft AzureMicroservicesAPI DesignSnowflakeDevOps+92
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SS

Saleem Sultan

Screened ReferencesStrong rec.

Director-level Product & Engineering Leader specializing in AI/ML, cloud platforms, and digital transformation

Dallas, TX10y exp
HCSCCornell University

“Senior engineering/technology leader who has defined and delivered a multi-year roadmap to modernize platforms and embed AI, leading global teams through cloud-native and microservices migrations on AWS/Azure. Demonstrated measurable outcomes including 99.99% uptime, 40% fewer incidents, 25% faster delivery, 5x scalability, and $30M in new business opportunities, while scaling a 100+ person distributed org with strong OKR-driven execution and mentorship culture.”

A/B TestingAgileAPI GatewayApache KafkaApache SparkAuthentication+289
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BS

Brian Spang

Screened ReferencesStrong rec.

Director-level Demand Generation & Digital Marketing leader in B2B SaaS and Cloud

Seattle, WA26y exp
Komodo HealthUniversity of Puget Sound

“Growth marketing and marketing ops leader who joined a PE-owned SaaS business after $150M in annual sales and built the demand gen function from the ground up (roles, processes, tech stack, and reporting). Implemented SiriusDecisions-based lead processes, Salesforce-driven lead scoring/routing, and multi-touch attribution, then enabled sales via Challenger training—driving $231M in pipeline and improving marketing ROI to $6 pipeline per $1 spent.”

A/B TestingAsanaBudget ManagementCampaign ManagementData GovernanceDigital Marketing+152
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JG

Jian Guo

Screened

Executive Data & AI Leader specializing in enterprise data platforms and analytics

NYC, NY25y exp
Bank of ChinaNYU

“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”

AnalyticsCloud ComputingCost OptimizationData AnalyticsData EngineeringData Governance+120
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DM

Debasish Mishra

Executive Engineering Leader specializing in Agentic AI and Generative AI

San Francisco, USA20y exp
mentorGenie.aiCornell University
Generative AIPredictive analyticsAnomaly detectionSentiment analysisModel fine-tuningData pipelines+63
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SA

Shilpa Akunuri

Executive Technology Leader specializing in SaaS platforms and Generative AI

San Francisco, CA18y exp
AmazonWharton School
A/B TestingAgileAngularAWSAWS CloudFormationAWS IAM+58
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KT

Kevin Tan

Senior Full-Stack Engineer specializing in AI/ML product engineering

Fort Lauderdale, Florida11y exp
NetflixFlorida State University
PythonTypeScriptSQLDjangoFlaskFastAPI+88
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PA

Puneet Arora

Director-level Engineering Leader specializing in personalization platforms, MLOps, and GenAI

San Francisco, CA17y exp
AmazonUSC
A/B TestingAnalyticsAnomaly DetectionAutomationCloud-Native ArchitectureCost Optimization+37
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VK

Vibha Kumar

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps

Cupertino, CA5y exp
OpenAIUniversity of North Carolina at Charlotte
A/B TestingAgileApache HiveApache KafkaApache SparkArgo CD+165
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AC

Ankit Chaudhary

Senior Data Engineer specializing in cloud data platforms and analytics pipelines

Seattle, WA11y exp
ConfluentIIT Kanpur
PythonSQLPL/SQLJavaScalaSnowflake+81
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HV

Harsha vardhan

Senior Data Engineer specializing in cloud-native data platforms and streaming pipelines

7y exp
GoogleUniversity of Cincinnati
Azure Data FactoryAWSAmazon S3AWS LambdaAWS GlueAmazon EMR+94
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KC

Kalyani Chittipolu

Screened

Mid-level Data Engineer specializing in AI/ML platforms and cloud data pipelines

USA4y exp
MetaTexas Tech University

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”

Machine LearningMLOpsData EngineeringData PipelinesETLData Ingestion+108
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