Hire the Best dbt Engineers

I can help you build your Cloud Data Platform. As a Senior Data Engineer ( 7× GCP | 2× Azure and Databricks Certified ) specializing in implementing Cloud Data Platform, building scalable data pipelines, optimizing ETL processes and transform data into insight. I'm also a Teaching Assistant, teaching NLP to postgraduate students, besides working on different NLP projects. Let’s collaborate 🤝 to transform your data into results! 📈

Full Stack Java Development | Python Backend | ETL Pipelines | SQL Optimization | Hadoop | Spark | Snowflake | dbt | Enterprise Systems I’m a 𝗨𝗦‑𝗯𝗮𝘀𝗲𝗱 𝗦𝗲𝗻𝗶𝗼𝗿 𝗝𝗮𝘃𝗮 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 with 𝟵+ 𝘆𝗲𝗮𝗿𝘀 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 building and scaling enterprise systems for Fortune 500 companies, including 𝗝𝗣𝗠𝗼𝗿𝗴𝗮𝗻 𝗖𝗵𝗮𝘀𝗲, 𝗙𝗼𝗿𝗱 𝗠𝗼𝘁𝗼𝗿 𝗖𝗼𝗺𝗽𝗮𝗻𝘆, 𝗮𝗻𝗱 𝗝𝗲𝘁𝗕𝗹𝘂𝗲. My work focuses on backend engineering, distributed data pipelines, and modernizing systems that need to perform reliably under real production load. I’ve spent my career designing backend architectures that stay fast, stable, and cost‑efficient as they grow. Whether it’s a Java microservice that needs to scale, a Python backend that needs to be production‑ready, or a data pipeline that has to process millions of records without breaking, this is the work I do every day. 𝗛𝗼𝘄 𝗜 𝗛𝗲𝗹𝗽 𝗧𝗲𝗮𝗺𝘀: 𝗝𝗮𝘃𝗮 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀: If your backend is slow, fragile, or stuck inside a monolith, I fix that. I work with Spring Boot, Spring MVC, Spring Security, JWT authentication, GraphQL, and microservices. I modernize legacy Java codebases, break apart monoliths, and build APIs that stay reliable when traffic spikes. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗮𝗻𝗱 𝗙𝗮𝘀𝘁𝗔𝗣𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: I build Python services using FastAPI, SQLAlchemy, asyncio, and Pydantic. I’ve delivered async microservices, automation workflows, and data processing jobs used in production at Ford and JetBlue. 𝗖𝗹𝗼𝘂𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: I design and deploy production environments on GCP, AWS, and Azure. I’ve built systems using Cloud Run, Pub/Sub, Dataflow, BigQuery, GKE, Lambda, S3, Redshift, Glue, and EKS. I use Terraform for infrastructure as code and set up CI/CD pipelines for clean deployments. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗘𝗧𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀: If your pipelines are brittle, slow, or producing outdated data, I rebuild them. I work with Apache Beam, Spark, Airflow, Kafka, dbt, Snowflake, and BigQuery. At Ford, I replaced a six‑hour Hadoop batch job with a Dataflow streaming pipeline that finished in under ten minutes. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: I tune SQL queries, redesign schemas, and optimize databases at scale. I’ve worked with PostgreSQL, SQL Server, MongoDB, MySQL, Redshift, Snowflake, and BigQuery. 𝗟𝗲𝗴𝗮𝗰𝘆 𝗦𝘆𝘀𝘁𝗲𝗺 𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: I migrate old systems without disrupting what already works. I’ve moved slow stored procedures into Spark and Hive, improving execution time across distributed file systems. 𝗔𝗣𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀: I build reliable integrations with third‑party APIs, handling authentication, pagination, rate limits, retries, and logging. I’ve worked with payment gateways, SaaS platforms, IoT systems, and enterprise data sources. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Java (8, 11, 17), Python, Spring Boot, Spring Security, Microservices, REST APIs, GraphQL, Swagger/OpenAPI 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Spark, Hadoop, Beam, Hive, Airflow, Spring Batch, Data Ingestion Pipelines 𝗖𝗹𝗼𝘂𝗱: GCP (Cloud Run, BigQuery, GKE), AWS (Lambda, S3, Redshift, EKS), Azure 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: PostgreSQL, SQL Server, MongoDB, MySQL, Redis, Snowflake 𝗗𝗲𝘃𝗢𝗽𝘀: Terraform, Kubernetes, Docker, CI/CD (GitHub Actions, Jenkins, Tekton) 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Grafana, Splunk, Cloud Monitoring The Right Fit: I work best with teams that need a senior engineer who can take ownership of complex backend or data engineering problems. If you’re building on GCP, AWS, or Azure and need someone who has worked in those environments at scale, I can help. I communicate clearly, deliver on time, and bring the same standards I used at Ford and JPMorgan. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝘆𝗼𝘂𝗿 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝗿 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, 𝘀𝗲𝗻𝗱 𝗺𝗲 𝗮 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗼𝗿 𝗰𝗹𝗶𝗰𝗸 “𝗜𝗻𝘃𝗶𝘁𝗲 𝘁𝗼 𝗝𝗼𝗯” 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. Spring Boot, Java Backend, SaaS Development, REST API, Microservices, AWS, Scalable Applications, SaaS Architecture, Platform Engineering, Distributed Systems, Event-Driven Architecture, Cloud-Native Development, Enterprise APIs, Multi-Tenant SaaS, AI Integrations, Kubernetes, Docker, Kafka, DevOps, System Design, Scalable Infrastructure, High Availability Systems, Fintech, Healthcare Platforms, Booking Engines, Real-Time System, Senior Data Engineer, Backend Engineer, Data Platform Engineer, Data Pipeline Development, ETL Development, ELT, Data Architecture, Data Engineering, Data Warehousing, Analytics Engineering, Big Data, Data Integration, Data Modeling, Database Design, Database Management, PostgreSQL, MySQL, MongoDB, Redis, SQL, Python

Data Integration specialist with over 19 years of experience in designing, building, maintaining and supporting Data warehouse and ETL applications. Extensive experience working on Snowflake, dbt, Datastage , Informatica, AWS, Unix/ Python Scripting. Experienced with relational databases - Snowflake, Netezza, DB2, Oracle and SQL server. Proficient in writing SQL queries to perform analysis and ELT loads into tables. Experience working on Bigdata tools – HDFS, Hive, Sqoop & Kafka. Exposure to Cloud technologies – ASW and Azure as data sources. Experienced working with diverse clients belonging to - Banking, Manufacturing, Restaurant and Retail domain. Good understanding of end-end architecture of Datawarehouse and applications worked on. Experienced in working on Agile (Scrum) & waterfall methodologies of project execution. Highly motivated, strong analytical skills, quick learner and interested to learn and work on new technologies.

Updated on 13/08/2026 Most dashboard problems are not dashboard problems. A number that does not match what someone counted by hand usually broke three steps earlier, in ingestion or a transformation nobody tested. That is where I actually spend my time as a Data and AI Engineer, and the chart at the end is the easy part. My pipelines typically run on Airflow or Mage AI. Anything that needs to move in real time goes through Kafka and PyFlink. On AWS I work with Lambda, S3, EventBridge, and SNS, and bad records get pulled into a quarantine bucket instead of quietly sitting in a table someone trusts. For transformation, I build dbt models on Snowflake and PostgreSQL, structured bronze through gold, with schema tests and business rule checks written into the models themselves so a broken assumption gets caught in the pipeline, not by whoever opens the report next. Reporting comes after the data is solid. I build in Power BI or Tableau around the one question the business actually needs answered, not a stack of generic rollups nobody reads. When the need is document search or research rather than dashboards, I build RAG systems that score their own retrieval accuracy, so a weak answer gets flagged instead of handed over as confident nonsense. I am currently applying this same thinking at Genix Pharma, building AI-assisted workflows on local LLMs through Ollama for model experimentation, evaluation, and automated reporting. A few things I have shipped recently: a district-level KPI dashboard on Snowflake and Power BI built from layered dbt models, incremental dbt pipelines feeding logistics and lending risk reporting, a real-time Kafka and PyFlink pipeline with event-time processing sinking to PostgreSQL, and a serverless AWS pipeline where a quarantine bucket keeps bad files from ever touching the tables people query. If a tool is not something I have genuinely used, I will say so instead of guessing my way through your job. Tell me what the reporting needs to answer and where your data lives right now, whether that is Excel, PDFs, or a handful of systems that do not talk to each other. I will give you a straight read on whether it is a small fix or a bigger rebuild. Machine Learning, Database Design, Delta Lake Expert, Databricks Engineer, Big Data Consultant, AWS Data Specialist, Database Architecture, Amazon Web Services, Artificial Intelligence, Deep Learning Modeling, Machine Learning Engineer, Data Analytics & Visualization Software, Data Warehousing & ETL Software Data Processing, Cloud Engineering, GCP Analytics, Data Analytics, Data Visualization, Spark Developer, ETL, SQL, Python, DBT, Snowflake, Apache Airflow, Apache Kafka, AWS, Data Pipeline, Power BI, Python, Snowflake, ETL, Big Data, ETL Pipeline, Data Engineer, ETL Developer, Data Science, Data Analysis, Deep Learning, Data Engineering, Azure Databricks, MLOps Engineer,

I build end-to-end data platforms — from raw source ingestion through dbt transformation to production dashboards — on Snowflake, BigQuery, Databricks, Sigma Computing, and Looker. One consultant who owns the full stack: ETL pipelines, cloud data warehouse, data modeling, and the BI reporting layer your team actually trusts. Founder of Warehows Analytics. Official partner with Snowflake, dbt, Databricks, and Sigma Computing. 32+ projects delivered across SaaS, e-commerce, fintech, healthtech, cybersecurity, and private equity. Top Rated Plus with 100% client satisfaction. $2.3M+ saved in client infrastructure costs. Stack: Snowflake, BigQuery, Databricks, dbt, Airflow, Fivetran, Airbyte, Hevo, Sigma Computing, Looker, Power BI, Superset, Streamlit, Python, SQL, FastAPI What I have delivered: - Fortune 500 — Oracle to Snowflake migration. 10M+ records/day CDC pipelines. Query times down 85%, costs down 60%. - $50M e-commerce brand — unified Google Ads, Facebook, email, and organic data. Single attribution model. ROAS from 3:1 to 7:1. - Finance SaaS (finsightsai.tech) — multi-tenant embedded analytics on Snowflake + Sigma serving 80+ customers. Sub-second queries. Built end to end: ingestion, dbt models, UI, LLM-powered insights. - Multi-brand e-commerce (4 brands) — Shopify, WooCommerce, QuickBooks, and ad platforms consolidated into Databricks + Looker. +65% marketing ROI. 80% less reporting time. - Cybersecurity startup — BigQuery warehouse consolidating Salesforce, RB2B, and product data. dbt-modeled attribution resolved three conflicting definitions of "converted lead." - Creative & PR agency — 50K+ customer reviews processed. AI sentiment analysis. Sigma dashboards. Client onboarding from 6 weeks to 1 week. - Veterinary clinic group — manual Excel to live cross-clinic dashboards. Airbyte + Airflow + dbt + Snowflake + Sigma. 10x faster onboarding. - Energy PE firm — Snowflake Cortex Analyst. Natural-language queries over sensitive portfolio data. - eMarketer — Snowflake reporting replacing 3-week manual Excel process. Runs daily, untouched. Services: - Cloud data warehouse design (Snowflake, BigQuery, Databricks) - ETL/ELT pipelines (Fivetran, Airbyte, Hevo, custom Python) - dbt transformation with testing, documentation, and semantic layer - BI dashboard development (Sigma, Looker, Power BI, Superset, Streamlit) - Embedded analytics for SaaS products (multi-tenant, row-level security) - Marketing attribution and revenue reporting - Data migration from legacy systems (Oracle, Talend, on-prem) - AI/LLM integration (RAG, conversational analytics, Cortex Analyst) Why this matters: data projects fail when nobody owns both ends. The pipeline engineer builds what was specified. The analyst reports what was delivered. Nobody checks whether either matches what the business needed. I hold the full picture — from raw source to executive dashboard — so the numbers your team sees are the numbers your finance team agreed to. Every model has tests. Every pipeline has monitoring. Every dashboard traces to a definition settled before the first chart was built. Send me a message. I respond the same day. Top Rated Plus · 32+ Projects · Snowflake Partner · dbt Partner · Databricks Partner · Sigma Computing Partner

Data Engineer | Data Analyst | Big Data Consultant I specialize in building scalable data solutions that streamline workflows, automate processes, and unlock valuable business insights. With over 4 years of experience, I have helped businesses integrate, transform, and visualize their data for data-driven decision-making. 🔹 What I Do: ✔ ETL & Data Pipelines – Automating data ingestion using Python, Fivetran, Airbyte, and Keboola ✔ Data Transformation – Structuring and optimizing data with DBT, SQL, and Dataform ✔ Data Warehousing – Managing large-scale databases in BigQuery, Snowflake, SQL Server, Postgres, and AWS Redshift ✔ Data Visualization – Creating insightful dashboards in Looker Studio, Power BI, Metabase, and Tableau 🔹 Data Sources I Have Worked With: ✔ Marketing Data – Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, Snapchat Ads, Reddit Ads ✔ CRM & Sales – HubSpot, Salesforce, Go High Level, SPP ✔ E-commerce – Shopify, WooCommerce, Amazon Seller Central ✔ Analytics & Tracking – Google Analytics, Mixpanel, Adjust ✔ Payments & Invoicing – Stripe, Xero ✔ Forms & Email Marketing – Typeform, Klaviyo, Active Campaign 🔹 Why Work With Me? ✔ I build automated, scalable, and efficient data solutions that save time and optimize business operations. ✔ My expertise in both data engineering and data analytics allows me to bridge the gap between raw data and actionable insights. ✔ I focus on clean, structured, and well-documented data solutions that are easy to maintain and expand. I’m here to help you automate, clean, and visualize your data so you can focus on growing your business. Let’s discuss how I can contribute to your success!