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Slow pipelines, unreliable data, or a warehouse that breaks every time the source changes? I build data systems that don't. I'm a 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗙𝗮𝗯𝗿𝗶𝗰 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 with 5 years of experience delivering end-to-end data engineering and BI solutions. I currently work at Pakistan's largest payment gateway — a high-volume fintech environment where 𝗧𝗕-𝘀𝗰𝗮𝗹𝗲 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮, strict governance, and zero tolerance for pipeline failures are the daily reality. My specialty is building systems that are 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝘁𝗮𝗿𝘁 — metadata-driven, layered, monitored, and built to scale. 𝗪𝗛𝗔𝗧 𝗜 𝗕𝗨𝗜𝗟𝗗 ✦ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗘𝗧𝗟/𝗘𝗟𝗧 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 Control logic lives in configuration, not hardcoded. One framework handles dozens of sources with built-in logging, error handling, and restartability. Proven: reduced ETL runtime by 𝟯𝟴% on a production enterprise warehouse by eliminating redundant mapping layers. ✦ 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 End-to-end warehouse design across 𝗦𝘁𝗮𝗴𝗶𝗻𝗴 → 𝗖𝗼𝗿𝗲 → 𝗚𝗼𝗹𝗱 (Medallion Architecture), with star/snowflake schema modeling, incremental loading, duplicate handling, and structured audit logging baked in. ✦ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 Lakehouse and Warehouse design on OneLake, Fabric Data Factory pipelines, semantic models with 𝗥𝗼𝘄-𝗟𝗲𝘃𝗲𝗹 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 (𝗥𝗟𝗦), and report publishing as Fabric Apps for internal teams and external stakeholders. ✦ 𝗔𝘇𝘂𝗿𝗲 & 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 ADF-orchestrated cloud pipelines and PySpark-based distributed data processing on Databricks for large-scale, partitioned datasets. ✦ 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 & 𝗦𝗦𝗥𝗦 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Semantic model design, DAX measures, drill-through dashboards, RLS enforcement, SSRS and Report Builder reports, and Fabric App deployment for enterprise stakeholders. ✦ 𝗟𝗲𝗴𝗮𝗰𝘆 𝗠𝗜𝗦 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Migrated 20+ reports from legacy systems into a centralized, modern BI architecture without disrupting ongoing operations. 𝗥𝗘𝗖𝗘𝗡𝗧 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 • Reduced ETL runtime by 𝟯𝟴% (4 hrs → 2.5 hrs) by optimizing metadata-driven SSIS pipelines • Built automated SFTP ingestion pipelines with archive logic to ensure 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹, 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲-𝗳𝗿𝗲𝗲 data loading • Delivered 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 supporting different business products across fintech, billing, and payments • Published 𝟭𝟱+ 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗿𝗲𝗽𝗼𝗿𝘁𝘀 as Fabric Apps with Row-Level Security for external stakeholders • Onboarded 10+ new source tables into a redesigned data warehouse while improving ETL performance and storage efficiency • Worked extensively with 𝗧𝗕-𝘀𝗰𝗮𝗹𝗲 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 in a high-volume payment processing environment. 𝗖𝗢𝗥𝗘 𝗦𝗧𝗔𝗖𝗞 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰 | 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 | 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 | 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 | 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 | 𝗦𝗦𝗜𝗦 | 𝗦𝗤𝗟 𝗦𝗲𝗿𝘃𝗲𝗿 | 𝗢𝗿𝗮𝗰𝗹𝗲 | 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 | 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 | 𝗦𝗦𝗥𝗦 | 𝗧-𝗦𝗤𝗟 | 𝗣𝗟/𝗦𝗤𝗟 | 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 | 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 | 𝗦𝘁𝗮𝗿 𝗦𝗰𝗵𝗲𝗺𝗮 | 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗦𝗰𝗵𝗲𝗺𝗮 | 𝗘𝗧𝗟/𝗘𝗟𝗧 | 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝗕𝗘𝗦𝗧-𝗙𝗜𝗧 𝗣𝗥𝗢𝗝𝗘𝗖𝗧𝗦 • Data warehouse or lakehouse design from scratch • ETL/ELT pipeline build, optimization, or troubleshooting • Microsoft Fabric or Azure migration from legacy on-prem systems • Power BI, SSRS, or Fabric App reporting solutions • SQL performance tuning, stored procedures, and indexing • Production pipeline monitoring, job scheduling, and failure resolution 𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞 I understand your business process, data sources, and reporting needs first. Then I design a practical architecture, build clean and observable pipelines, validate the data, and deliver reporting-ready models your team can actually trust — with 𝗹𝗼𝗴𝗴𝗶𝗻𝗴, 𝗲𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴, and 𝗷𝗼𝗯 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 built in from day one, not added as an afterthought. 📩 𝗦𝗲𝗻𝗱 𝗺𝗲 𝗮 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 — 𝗜 𝗿𝗲𝘀𝗽𝗼𝗻𝗱 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝗮𝗻𝗱 𝘄𝗶𝗹𝗹 𝗼𝘂𝘁𝗹𝗶𝗻𝗲 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁.

I am a Senior Data Engineer & Data Analyst with over 7 years of hands-on experience across SQL Databases, Data Engineering, ETL Pipelines, Cloud environments, Data Analytics and Data Warehousing. I deliver end-to-end data solutions, seamlessly bridging the gap between backend ETL Pipelines and frontend business insights to help companies make faster, better decisions. WHAT CAN I DO FOR YOU: • Build ETL and ELT pipelines to centralize fragmented business data. • Design data warehouses and dimensional models (star and snowflake). • Optimize complex SQL queries and improve database performance using standard data optimization techniques. • Develop interactive, automated dashboards and reporting suites for business stakeholders using Power BI, MicroStrategy, Superset, and Qlik. • Implement cloud data solutions using the Microsoft Azure ecosystem (Data Factory, Synapse, Fabric, Data Lake). • Clean, structure, format, and prepare massive, messy datasets for deep data analytics. TECHNICAL STACK: • Cloud & Warehouses: Azure (Data Factory, Synapse, Fabric, Azure SQL, ADLS), Snowflake, Teradata. • ETL Tools: Azure Data Factory, Talend Open Studio, Pentaho, Informatica, dbt, Apache NiFi. • BI & Analytics: Power BI, MicroStrategy, Apache Superset, Qlik, Looker Studio, Power Query. • Databases: PostgreSQL, MySQL, SQL Server, Oracle, Teradata, MongoDB, Cassandra. • Languages: SQL (T-SQL, PL/SQL), Python (Pandas, NumPy), PySpark. • Modeling: Data Modeling (ERD, Star Schema, Snowflake Schema). WHY CLIENTS HIRE ME: • 30+ successful data projects completed across engineering and analytics domains. • Strict focus on performance, long-term scalability, and clean, debt-free data architecture. • Clear, transparent communication and reliable, on-time delivery. • True end-to-end data expertise, tracking your data lifecycle from raw pipeline to final insight. If you need a reliable specialist who can streamline your data pipelines and turn raw tables into clear business insights, let’s talk. Click the "Invite to Job" or "Message" button to discuss your project goals.

I'm Danish Sohail, a seasoned data professional with over a year of hands-on experience across a wide spectrum of technologies. My expertise includes: 𝐒𝐐𝐋 : I possess a strong command of SQL, enabling me to write efficient queries for data extraction from various database systems. This ensures your data is primed for analysis, whether you're using SQL Server, MySQL, PSQL, Oracle, or other platforms. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬: Proficient in creating interactive dashboards and reports using Power BI, I can connect to diverse data sources, design comprehensive data models, develop custom visualizations, and implement complex DAX calculations and measures to provide valuable insights into your data. 𝐓𝐚𝐥𝐞𝐧𝐝 𝐄𝐓𝐋 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: I specialize in designing, developing, and deploying ETL (Extract, Transform, Load) solutions using Talend. Whether you need to consolidate data from multiple sources, cleanse and transform it, or load it into your target system, I can create a scalable, efficient, reliable and fully generic ETL solution tailored to your specific needs. 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲: I'm well-versed in building data integration pipelines using Azure Data Factory. I can help you design and implement scalable, dependable data integration solutions that align with your business requirements and objectives. 𝐀𝐖𝐒 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: I've developed expertise in Amazon Web Services (AWS) cloud services, including SNS, SQS, S3, ECR, ECS, Lambda, and Kinesis Firehose. This experience enables me to effectively manage cloud-based projects with a keen focus on efficiency and security. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: With hands-on experience in Python for data science and analytics, I proficiently manipulate, analyze, and visualize data, creating customized data analytics scripts to address your specific requirements. Additionally, I implement automated alerts to enhance data monitoring and responsiveness. 𝐏𝐨𝐰𝐞𝐫 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Proficient in creating workflows and automating business processes using Power Automate, I can assist you in automating data entry, approval workflows, notifications, and more, streamlining your business operations for greater efficiency. As a proactive, collaborative, and results-driven professional, I'm deeply committed to helping clients unlock the full potential of their data. If you're seeking a data professional with a diverse skill set and a proven track record of delivering results, please don't hesitate to reach out. I look forward to discussing how I can contribute to your data success.

Updated on 07/08/2026 Most dashboard problems are not dashboard problems. A number that does not match what someone counted by hand usually broke three steps earlier, somewhere 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. 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. 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, 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,

Most data problems show up the same way, whether it's a spreadsheet someone updates by hand every week or a pipeline that's quietly started giving numbers nobody trusts. I build and fix ETL pipelines, warehouses, and API integrations on GCP and AWS, from replacing manual copy-paste with automated pulls to cutting a client's warehouse costs by ~50%. Tell me what's manual or what feels off, and I'll give you a straight read on what it'll take to fix it. Over the last 3 years I've delivered 55+ data projects across finance, healthcare, energy, and e-commerce, from one-off ETL jobs to platforms processing billions of records a day. I work GCP-first (BigQuery, Airflow, dbt, Dataflow), and I'm comfortable across AWS, Postgres, and the messy real-world stack most teams actually have. What I build: - End-to-end ETL/ELT pipelines in Python, SQL, Airflow, and dbt - BigQuery / Snowflake / Redshift warehouses and data models that stay clean as they grow - Migrations off legacy jobs and on-prem databases — without losing data in the move - Metabase, Looker Studio, and Power BI dashboards your team will actually open - Query and cost optimization when your warehouse bill stops making sense - API integrations with proper logging, retries, and checkpoints, so failures are visible instead of silent You probably need me if: - Your pipelines break and you hear it from a stakeholder, not an alert - Reports run slow, cost too much, or quietly disagree with each other - A previous developer left and nobody fully understands the setup anymore - You're scaling fast and the current data stack is starting to crack A few real results: - Architected pipelines processing 5B+ records daily at 99% reliability - Cut a client's warehouse costs ~50% by migrating legacy jobs to BigQuery - 4× throughput and 70% faster ingestion on an API pipeline pulling 2K+ domains a day - 40% faster pipeline runs through Airflow optimization How I work: a clear yes/no on feasibility before you commit, regular updates, and no disappearing mid-project. Most clients come back — usually because fixing one thing surfaces the next. If that sounds like your situation, send a short note on what's breaking or what you're trying to build, and I'll tell you straight what it'll take.

I am a tech-agnostic data engineer who enjoys solving complex data problems, finding patterns in messy datasets, and making sure the work actually supports the business. My focus is always on clean architecture, performance, and making data useful - not just moving it around.Here is what I have worked with: - Programming & Scripting: Python (Flask, FastAPI, Django, Selenium, PySpark), SQL - Data Engineering & Integration: Talend, Mage.ai, Apache Airflow, Airbyte, Databricks, Kafka, Debezium - Databases & Data Warehousing: Data Modeling, PostgreSQL, MySQL, Snowflake, Greenplum, MongoDB, Firebase, ClickHouse - Cloud & DevOps: AWS (Lambda, API Gateway, S3, RDS, SQS and more), Docker, Kubernetes, Azure DevOps, Terraform, CloudFormation - Security & Compliance: Secure and compliant pipeline design (HIPAA, GDPR, SOC 2), data governance, privacy-first architectures - Testing & Automation: Postman, Cypress.io, Swagger, Automated Data Workflows - Data Visualization: Power BI, Metabase, Google Data Studio, Grafana - Collaboration & Leadership: Cross-functional team mentoring, process optimization, data-driven decision-making