Data Warehousing Experts 2025

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.

WareGo is a leading provider of enterprise-grade Warehouse Management Systems (WMS) and supply chain technology, dedicated to eradicating inventory chaos through intelligent automation. Founded with a vision to simplify logistics, WareGo has grown from its early roots in 2004 to become a global force supporting over 200 customers across 30+ industries, including e-commerce, 3PL, and manufacturing. The team at WareGo comprise a passionate team of over 150 logistics experts and engineers committed to delivering 99.9% inventory accuracy for their clients.

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.

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.

Will Turner is the Founder and Head of Data & AI at Beyond Data, an Australian consultancy specialising in insight-to-action systems, the architecture that closes the gap between what a business's data knows and what the business actually does. Headquartered in Brisbane and serving clients across Australia and New Zealand, Will and his team design, build, and run AI agents, data lakehouses, and advanced analytics with a ruthless focus on commercial outcomes, not proofs of concept. Will has successfully delivered AI and data projects for 15+ businesses across finance, manufacturing, retail, agriculture, healthcare, construction, insurance and digital marketing. His work touches thousands of people each day. His key insight, that SaaS delivers actions without insight, and analytics delivers insight without action, is gaining traction with Australian technology leaders as they start to adopt his insight-to-action systems methodology. With more than a decade in data and AI, a post-graduate qualification in Data Science from James Cook University, and multiple Head of AI roles, Will brings an authentic human lens to production AI. He is the Amazon-bestselling author of Even YOU can learn Data (2022) and a 2017 Queensland Young Achievers Award nominee. Will writes and speaks on AI strategy, data leadership, and why most AI projects quietly fail.

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

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.

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.

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’m most effective in the space between business and technology—comfortable moving as deeply into each as the problem requires. My core strength is data: translating messy operational reality into reliable, explainable outputs that leaders can trust.I’ve delivered results across ERP and manufacturing environments—filling gaps, untangling legacy logic, and building practical bridges from the planning layer to the plant floor. I communicate clearly to all audiences and I’m happiest when the problem is ambiguous and the stakes are real.I’m a consultant by nature: I learn the business fast, diagnose what’s structurally broken, and build durable solutions through business, data, process, and technical analysis. Proven success as both a leader and hands-on builder.(Professionally prepared Summary)Experienced Business Systems, Process, and Data Analyst with excellent written and verbal communication skills, allowing for the clear expression of complex, technical materials in a manner comprehensible to a wide variety of stakeholders. Possesses proven ability to efficiently and effectively absorb business processes, communicate with technical staff, and deliver solutions including potential changes and improvements to processes and systems. Demonstrated passion for both the efficient and accurate collection of data as well as the transformation of data into information that can be leveraged by key decision makers for setting strategy and driving profitability. Proven success managing projects and resources, both as a Project/Program Manager and as a Team Lead.

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)

A data-analytics entrepreneur, thought-leader and the Founder & CEO of Perceptive Analytics - a company that helps businesses turn raw data into actionable insights, smart marketing decisions, and clear visual analytics. With an MBA from ISB and advanced training from Stanford’s Graduate School of Business, he brings together strong academic grounding and real-world business experience. Prior to founding his own venture in 2010, he sharpened his analytical and consulting skills at top firms including Infosys. Over the years, he has helped global clients, including Fortune-500 firms unlock value from their data, driving growth, optimization, and intelligent decision-making. Under his leadership, Perceptive Analytics has been recognized among the “top analytics companies to watch.” Passionate about data, strategy and storytelling with numbers, he firmly believes in empowering businesses to make smart, evidence-based decisions.

Srujana Sree Bathineni is a Lead Data & AI Platform Architect with expertise in enterprise AI, data platforms, software architecture, and technology transformation. She designs and develops AI and data enabled platforms that connect business data, automation, analytics, and intelligent capabilities with real world operational workflows. Her experience spans architecture, engineering, data, AI, and cross functional technical leadership, with a focus on turning complex technology challenges into practical and scalable solutions. She regularly works at the intersection of AI adoption, enterprise architecture, data engineering, and software development.

I’m a Data Intelligence Engineer with 14+ years of experience designing and optimizing data platforms, pipelines, and analytics solutions. I specialize in scalable ETL/ELT workflows, ML integration in DevOps, and advanced data wrangling using graph technologies and knowledge graphs. At Dell, I’ve led automation efforts that improved ETL efficiency by 30%, implemented enterprise-wide data strategies, and ensured data integrity across complex systems. My technical toolkit includes Python, SQL, Spark, Airflow, Kafka, dbt, and cloud platforms like AWS and GCP. I’ve delivered impactful data governance, quality monitoring, and BI solutions at RAPP, Tech Mahindra, and Ericsson—driving insights across marketing, finance, and telecom sectors. Beyond the code, I’m a self-motivated, goal-oriented professional who thrives on solving complex challenges. I’m also a nature lover who believes in balance—whether it’s in data systems or life outdoors.

Software Data Engineer with over 9 years of experience who is passionate about turning big data into strategic assets. Experience Highlights: PySpark Development Dashboard Reporting AWS ETL Development ETL Data Validation Data Scrubbing REST API Data Ingestion

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.

Cyber Security Consultant and Tech Enthusiast with 10+ years of experience helping businesses strengthen digital security, optimize technology strategies, and drive innovation across the Cyber Security, Business Consulting, Technology, and SaaS industries. Skilled in identifying security risks, implementing resilient solutions, and advising organizations on secure digital transformation initiatives. Passionate about emerging technologies, PKI, cloud security, SaaS ecosystems, and helping startups and enterprises build scalable, secure, and future-ready systems. Known for combining technical expertise with business insight to deliver practical solutions that enhance operational efficiency, compliance, and cybersecurity resilience.

I’ve been working in data and AI for more than 10 years, leading teams and building systems that help businesses actually get value out of their data. At Innowise, I head the Big Data, AI/ML, and Python departments, which means I set direction for our projects but also stay hands-on with architecture, data workflows, and the tough technical decisions. Instead of just listing tools, I’d say my strength is knowing how to choose and combine them to solve real problems. Whether it’s Spark, Databricks, Snowflake, or cloud platforms like AWS and Azure, I treat them as building blocks, not buzzwords. I’ve worked in eCommerce, fintech, gaming, and manufacturing, and what I enjoy most is seeing messy, scattered data turn into clear insights that actually change how a business works. Outside of work, I’m into sports and racing. I like the mix of speed and strategy. I’m also fascinated by physics and technology, especially where they overlap to shape the future of AI and data.

Anusha Kovi is a Business Intelligence Engineer at Amazon, with 6+ years of experience. She builds and scales enterprise-grade data platforms that power analytics, operational intelligence, machine learning, and data-driven decision-making across large, distributed systems. Her specialty is end-to-end data engineering: designing ETL pipelines and analytics-ready data models, orchestrating workflows with Apache Airflow and AWS, building cloud data warehousing solutions, integrating MLOps capabilities, and delivering production data pipelines and dashboards that turn raw data into clear insights and measurable business outcomes. Beyond her engineering role, Anusha is an IEEE-published author, a technical writer on DZone and Hackernoon, and a conference speaker with work accepted at international research conferences and industry events. She focuses on the evolving intersection of AI and data engineering, helping organizations design resilient, AI-ready BI infrastructure and guiding engineers to thrive in an AI-native future, while staying deeply committed to community impact. She has mentored 200+ students, is a frequent judge and evaluator for student and industry innovation programs, and is passionate about making data engineering more accessible, advancing women in technology, and amplifying knowledge-sharing through writing, speaking, and mentorship. Google Scholar: https://scholar.google.com/citations?user=3pwdexMAAAAJ&hl=en Dzone: https://dzone.com/users/5466033/anukovi.html Hackernoon: https://hackernoon.com/u/anushakovi