Trusted Data Engineering Experts

As a Cloud Data Engineer, I specialize in designing and implementing scalable data pipelines, data warehouses, and data lakes. I have a strong understanding of data modeling techniques and warehousing principles, which I use to build efficient, high-performance solutions. I’m always energized by new challenges that require a mix of technical strategy and creative problem-solving. Whether I’m designing a new reporting data source or finally perfecting a home organization project, I’m driven by the same goal: creating systems that are as efficient as they are reliable.

Abhilash Rao Mesala is a Senior Data Engineer at Meta Platforms where he works on large-scale data infrastructure powering critical business intelligence across the organization. He has led data platform and pipeline initiatives across healthcare and enterprise environments, with a focus on scalable architecture and high-stakes production data systems. He writes at the intersection of data engineering and the operational complexity of building systems that organizations depend on to make consequential decisions.

I'm a Senior Data Engineer with 10+ years building production data platforms, currently leading a SOX and HIPAA-compliant Databricks Lakehouse on AWS at EverCommerce. The work spans consolidating data across 45+ operating companies on AWS, Azure, BigQuery, and Redshift into one governed source of truth for finance, billing, and executive reporting. A few things I've shipped recently: re-architected BigQuery dbt models into incremental merge chains and cut per-run cost by ~99.85% (projecting $113K in annual savings), and led an internal hackathon project on Airflow metadata observability that won 3 prizes and is moving to production. Before EverCommerce, I led an offshore engineering team at Hyundai AutoEver on a vehicle diagnostics big-data platform, owned end-to-end data engineering at Grabango (autonomous checkout retail), productionized ML pipelines at Nike with Kubeflow and SageMaker, and built CDC-driven financial datasets at Thrive Market. Happy to help on questions around: 1. Lakehouse and data platform architecture (Databricks, Unity Catalog, Delta Lake, dbt) 2. AI-ready data infrastructure for ML and GenAI 3. Multi-cloud data consolidation and post-acquisition integration 4. SOX and HIPAA compliance in data engineering 5. Pipeline cost optimization at scale 6. Orchestration trade-offs (Airflow, Dagster, dbt) 7. Streaming and CDC ingestion (Kafka, Fivetran, AWS DMS)

Principal Data Operations Engineer specializing in data governance and cloud database architecture across regulated financial services and insurance. I built an automated classification system that labelled 1.5 million database columns and bound protection to the result, work featured by Dagster and published as a first-author IEEE paper. I can speak to sensitive-data classification at scale, the cost and accuracy tradeoffs of using LLMs for governance work, access-control architecture and how it decays, and what actually happens inside a platform team when an industry-wide breach is announced. IEEE Senior Member. 17 years in the field.

I'm an AWS Data Engineer with 5+ years building production-grade ETL pipelines, cloud data warehouses and migrations on AWS. I work remotely with teams worldwide and have delivered end-to-end across banking, utilities, aerospace and FMCG. My sweet spot is AWS data engineering: ETL/ELT orchestration, Redshift and Snowflake warehouses, and pipelines that are cost-conscious by default and honest about their own data quality. Recent work includes a tier-1 bank's Sybase-to-Redshift migration and cutting an AWS Glue bill without touching business logic.

Senior technology leader with 20+ years of experience designing and delivering enterprise-scale data platforms, data lakes, Lakehouse and data mesh architectures. Proven expertise in building cloud-native, multi-tenant analytics platforms on Azure and AWS enabling scalable, governed, and high-performance data ecosystem leveraging data engineering, analytics , Machine learning and AI technologies.

I'm a Cloud Data Platform Engineer with more than 10 years of experience building data platforms and cloud infrastructure for enterprise organizations, primarily in the financial services industry. Over the years, I've worked on large-scale data migration, cloud modernization, data engineering, and AI-ready platforms using technologies such as Google Cloud Platform, BigQuery, Apache Spark, Kubernetes, and Terraform. I'm passionate about solving real engineering problems and helping organizations build secure, scalable, and reliable data platforms that support analytics and AI. I enjoy sharing practical lessons from projects I've worked on, especially around cloud architecture, data engineering, AI infrastructure, security, and modern platform design. Outside of my day-to-day work, I'm an IEEE Senior Member and an ACM-trained peer reviewer and have served as a reviewer for SASIGD 2026 and as a judge for DSH Hacks V1. I regularly write about cloud and data engineering, have a Scopus-indexed conference paper accepted with additional papers under review, and enjoy speaking and contributing to the technology community whenever I can.

I am a Data and AI leader with over 12 years of experience across data engineering, data science, analytics, and business intelligence. I design and scale data systems that support reporting, AI, and real-world decision-making, with a focus on data quality, architecture, and AI readiness. My work spans complex enterprise environments, where I help teams avoid data failures that undermine analytics and AI initiatives. I write and speak about practical data engineering, applied data science, AI readiness, and building data foundations that actually work.

I turn complex data into clear decisions — building platforms that don't just work, but transform how organizations think

I am a data and AI engineering leader with over a decade of experience building scalable data platforms and production-grade AI systems. My expertise spans real-time data pipelines, data modeling, and retrieval-augmented AI architectures, with a focus on making AI systems reliable, interpretable, and aligned with real-world decision-making. I have led the development of end-to-end data products across cloud environments, bridging data engineering and applied AI to deliver measurable business impact. My work emphasizes practical challenges such as data quality, system design, and operational scalability, areas that often determine success in production AI. I also contribute to the data and AI community through technical writing, conference speaking, and judging hackathons and industry awards. I regularly comment on topics including modern data engineering, AI in production, data quality, and the evolving role of AI in enterprise systems.

I do data engineering.

Founder of Sovereign Forger, building born-synthetic financial data for AI training and compliance testing. Our math-first pipeline generates UHNWI and KYC/AML profiles from Pareto distributions and algebraic constraints -- zero real data input, zero re-identification risk. 1.3 million records produced, zero balance-sheet errors. Expert in GDPR Article 25 data protection by design, EU AI Act Article 10 training data governance, DORA resilience testing, and PCI DSS 4.0 pre-production data requirements. Published research on SSRN covering the born-synthetic methodology for regulatory-compliant data generation.

I'm a Senior Engineering Leader and Architect at a top-tier FAANG company, with 18 years of experience building and scaling systems that sit at the intersection of data, AI, and consumer-grade reliability. My career spans Honeywell, 11+ years at Microsoft, and my current role architecting a real-time streaming platforms that powers personalization for hundreds of millions of users. I work at the architecture layer where engineering judgment meets business outcomes — designing for data completeness, fault tolerance, and cost at petabyte scale, while growing the engineers around me. I've led teams of 30+, mentored from senior to junior levels, and 1,000+ hours mentoring engineers across seven countries through ADPList. My technical work has been recognized through senior memberships and fellowships in major professional bodies — IEEE Senior Member, BCS Fellow, Distinguished Fellow at SCRS, and Royal Fellow of IAOSD — alongside service on Technical Program Committees for several international conferences. I'm a frequent keynote speaker on data platforms and trustworthy AI, and I'm author of two techincal books on Data Governance and Database technology. I'm continuing my leadership development through Wharton Executive Education, with a focus on the bridge between deep technical execution and enterprise strategy. Whether I'm writing, speaking, mentoring, or shipping, my throughline is the same: build Data and AI systems that are fast, honest, and worth trusting.

Director of Engineering at Hudson Data with 14+ years building production systems for fraud detection, identity risk, and real-time decisioning. Inventor on US Patent 11,922,421 B2 (graph-based entity resolution for fraud detection). Led architecture and delivery of a sub-second fraud decisioning platform processing 8M+ application decisions monthly across multi-tenant GCP infrastructure serving fintech lenders, banks, and insurers. Expertise spans graph analytics, ML/AI model operationalization, rules engines, case management, and workflow orchestration in regulated financial environments. Previously held engineering leadership roles at Pegasystems, American Express, and other financial technology firms. Published in Finextra on real-time fraud decisioning architecture.

I'm the CTO of Kleene.ai, an AI data platform that unifies ingestion, transformation, analytics and decision intelligence in one place. I've spent close to 15 years in engineering and infrastructure — leading DevOps, cloud and security teams at Legal & General Investment Management, WorldRemit and Amido before joining Kleene.ai, where I've held Head of DevOps and Head of Cloud & Security roles and now lead technology as CTO. My focus today is helping businesses deploy AI safely and usefully: knowing where it genuinely creates value, where human judgement must stay in control, and where security, governance and clean data can't be compromised. I write and speak on generative and agentic AI, AI governance and guardrails, DevSecOps and software supply-chain security, and how teams modernise their data stack to move from questions to action.

Janani Annur Thiruvengadam is a Senior Data Engineer at Amazon, where she builds and scales enterprise-grade data platforms powering analytics, machine learning, and decision-making across large-scale distributed systems. She specializes in cloud data warehousing, MLOps integrations, and production data pipelines on AWS. Beyond her engineering role, Janani is an IEEE Senior Member, a published technical author on DZone, and a conference speaker with research accepted at international IEEE conferences. Her work explores the evolving intersection of AI and data engineering — helping organizations design resilient, AI-ready data platforms and guiding engineers to thrive in an AI-native future. She is passionate about mentoring, knowledge-sharing, and empowering women in technology through speaking, writing, and community engagement. LinkedIn- https://www.linkedin.com/in/janani-annur-thiruvengadam-0047b686/ IEEE papers - https://arxiv.org/abs/2512.23597 Dzone articles - https://dzone.com/users/5421773/jananipc.html

I'm the CEO of Kleene.ai, an AI data platform that turns scattered business data into clean, AI-ready foundations and decision intelligence. I'm an entrepreneur, advisor and chair who has spent my career building and scaling technology and digital businesses, and I studied at the University of Warwick. I think and write a lot about data sovereignty and the risks of building a business on a single LLM, the gap between AI hype and the infrastructure that actually makes AI useful, and what the UK and Europe need to do to compete on AI. I'm happy to talk about AI strategy and adoption, data sovereignty and vendor lock-in, the economics of the LLM market, and how businesses turn data into faster, better decisions.

Durga Prasad is a Senior Technical Architect specializing in enterprise backend architecture, Google Cloud Platform (GCP), and microservices governance. He builds scalable enterprise systems, leads database modernization initiatives, and develops frameworks for deterministic AI execution.

Executive-level Management Consultant specialising in driving business change and improving operational performance through the practical delivery of data insights and technology solutions. A Fellow of the Chartered Management Institute, and a Full Professional Member of the Chartered Institute of IT. After initially training with the Royal Navy as an Aircrew Officer, I went on to study mathematics at university before entering the financial services industry; joining one of the UK's leading asset management companies. I delivered business change programmes and led internal technology and finance teams, before joining Techmodal (now part of BAE Systems), a boutique consultancy specialising in digital transformation and applied data science in the Public Sector. In 2021 I joined Alchemmy, an award-winning management consultancy, to continue delivering digital and data programmes to help organisations grow and thrive.

Sandip Patel is a Enterprise Cloud and AI Security Expert specializing in enterprise AI, agentic systems, and secure cloud platforms. With 20+ years of experience, he helps Fortune 500 organizations design and deploy scalable AI solutions that balance innovation with governance, security, and compliance. His expertise spans Generative AI, Retrieval-Augmented Generation (RAG), and emerging agentic AI architectures, with a strong focus on building private, enterprise-grade AI systems on Azure. Sandip works closely with business and technology leaders to modernize legacy systems, integrate intelligent workflows, and operationalize AI in regulated industries such as financial services and healthcare. Recognized for his ability to translate complex technologies into real-world business outcomes, Sandip regularly advises on topics including AI governance, Zero Trust architecture, responsible AI, and enterprise digital transformation. He is also an active contributor to research, open-source initiatives, and industry discussions on the future of AI and cloud computing.