Hire the Best Talend Data Integration Specialists

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 am expert in data integration using Python, Snowflake, Talend Data Integration, database administration and processing xml files (xpath, xslt, xquery). I am master in business intelligence and specialist in process software development My experience with Talend Open Studio, SSIS, SSAS, SSRS started in 2006

¸¸♬·¯·♪·¯·♫¸¸ 𝗪𝗲𝗹𝗰𝗼𝗺𝗲 𝘁𝗼 𝗺𝘆 𝗽𝗿𝗼𝗳𝗶𝗹𝗲! ¸¸♫·¯·♪¸♩·¯·♬¸¸ I'm a Senior Data Engineer & Cloud Data Architect. I bridge the gap between fragmented raw data and high-performance, analytics-ready infrastructure. Whether you need to build a scalable data warehouse from scratch, transition from legacy ETL to modern dbt/Databricks stack, optimize costly cloud queries, or power real-time AI/ML applications, I specialize in architecting reliable, zero-downtime data pipelines across AWS, GCP, Azure, Snowflake, and BigQuery. ⚡ 𝐂𝐨𝐫𝐞 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 1. End-to-End Modern Data Stack (MDS) & ETL/ELT Pipelines Designing and deploying automated, resilient pipelines that extract, clean, transform, and load petabyte-scale data into centralized analytics hubs. ◾ Batch & Stream Ingestion: Building automated ingestion jobs from SaaS applications, REST APIs, webhooks, and legacy DBs using Fivetran, Airbyte, Kafka, and Debezium (Change Data Capture - CDC). ◾ Analytics Engineering: Modular, version-controlled transformations with dbt (Data Build Tool), custom SQL, and PySpark-complete with automated documentation and lineage tracking. ◾ Workflow Orchestration: Designing DAGs, automated retries, and monitoring alerts using Apache Airflow, Prefect, Dagster, and AWS Step Functions. 2. Cloud Data Warehousing & Lakehouse Architecture (Snowflake, Databricks, BigQuery) Structuring high-efficiency, cost-optimized databases designed for instant analytical querying and BI dashboard performance. ◾ Warehouse Optimization: Clustering keys, partitioning, materialization, micro-partitioning, and query tuning to cut monthly cloud compute/storage costs by 30%–60%. ◾ Lakehouse & Open Table Formats: Architecting Delta Lake, Apache Iceberg, and Hudi layers on AWS S3/GCP Cloud Storage using Medallion Architecture (Bronze -> Silver -> Gold). ◾ Data Modeling: Dimensional modeling (Kimball methodology), Star/Snowflake Schemas, Data Vault 2.0, and Wide Flat Tables (OBT) optimized for Looker, Tableau, and PowerBI. 3. Real-Time Data Streaming & Event-Driven Systems Enabling millisecond-latency processing for live dashboards, fraud detection, dynamic pricing, and real-time operational metrics. ◾ Event Streaming: Setting up Apache Kafka clusters, AWS Kinesis, GCP Pub/Sub, and RabbitMQ with event serialization (Avro, Protobuf). ◾ Real-Time Analytics: Developing continuous stream-processing engines using Apache Flink, Spark Streaming, and ClickHouse/RisingWave for immediate insight delivery. 4. Data Quality, Governance, MLOps & AI Infrastructure Ensuring every byte of data entering your reporting systems is accurate, secure, compliant, and ready for advanced analytics or LLM applications. ◾ Data Quality & Observability: Automated schema validation, anomaly detection, and data testing using Great Expectations, Soda, and dbt test suites. ◾ AI/ML Infrastructure: Vector database setup (Pinecone, Weaviate, Qdrant, Milvus), RAG pipeline data ingestion, and feature store integration (Feast) for AI model training. ◾ Governance & Compliance: Role-Based Access Control (RBAC), Column/Row-level masking, PII obfuscation, and automated lineage mapping for GDPR/HIPAA compliance. ⚡ 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝗼𝗿𝐤𝐬 𝐈 𝐇𝗮𝘃𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 - 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Apache Airflow, Prefect, Dagster, Mage, AWS Step Functions, MWAA - 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 & 𝗘𝗻𝗴𝗶𝗻𝗲𝘀: Snowflake, Google BigQuery, AWS Redshift, ClickHouse, Trino/Presto, DuckDB - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 / 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Databricks, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue, PySpark, Apache Spark - 𝗘𝗧𝗟 / 𝗘𝗟𝗧 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: dbt (Core & Cloud), Airbyte, Fivetran, Kafka Connect, Debezium, Meltano - 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 & 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴: Apache Kafka, AWS Kinesis, GCP Pub/Sub, Apache Flink, Spark Streaming, RabbitMQ - 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 (𝗡𝗼𝗦𝗤𝗟 & 𝗥𝗗𝗕𝗠𝗦): PostgreSQL, MySQL, MongoDB, Redis, Cassandra, DynamoDB, Pinecone, Qdrant - 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 & 𝗦𝗾𝗹: Python (Pandas, Polars, PySpark, SQLAchemy), SQL (Advanced Dialects), Scala, Bash, Go - 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗗𝗲𝘃𝗢𝗽𝘀: Terraform, Docker, Kubernetes, AWS (S3, EC2, ECS, Lambda), GCP, Azure, GitHub Actions, CI/CD - 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Great Expectations, Soda, Monte Carlo, OpenLineage, Datahub ⚡ 𝗛𝗼𝘄 𝗜 𝗪𝗼𝗿𝗸: I am hired to design production-grade data pipelines, modernize legacy data stacks, fix slow analytics queries, and bring software engineering best practices (Git, CI/CD, unit testing, modular code) into data infrastructure. I prioritize clean lineage, cost efficiency, ironclad data security, and zero-downtime migrations. ⚡ 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲: ✔️ Pipelines built to scale ✔️ Massive cloud bill reduction ✔️ Production-grade reliability ✔️ Software engineering rigor ✔️ Clear communication 👉 𝗖𝗹𝗶𝗰𝗸 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 - 𝗹𝗲𝘁'𝘀 𝘁𝗮𝗹𝗸.

I help ecommerce brands, restaurant chains, and retail groups stop guessing and start reading their numbers at a glance. With 8+ years in data and BI, I turn messy Shopify, POS, and store data into a clean warehouse, dashboards your team actually uses, and an AI copilot that answers business questions in plain English. Here is what makes my work different: I add a chat-with-data layer on top of governed analytics. Instead of learning Power BI, a store manager or founder can just ask "how did each region do vs last month?" and get the right answer, backed by real SQL. The AI runs on a local model on your own infrastructure, so your data never leaves your environment and there are no per-query API fees. Every metric is defined once and protected by automated tests, so numbers do not silently drift between reports. A bit of my background: Senior BI Developer building end-to-end enterprise Power BI: dashboards, semantic models, star-schema data models, DAX optimization, and row-level security. Senior Data Analyst at Pizza Hut Vietnam, where I led the reporting redesign from SSRS to Power BI and grew internal BI adoption by 82% year over year. I also built RFM segmentation, churn prediction, pricing models, and demand forecasting, and trained restaurant managers directly. Independent analytics engineering: full pipelines using dbt, Airflow, and WrenAI text-to-SQL on a local LLM, delivered as governed, self-hosted platforms. Core stack: SQL (advanced), Snowflake, BigQuery, PostgreSQL, Python (pandas), Power BI, Superset, dbt, Airflow, and WrenAI for natural-language analytics. Strengths in dimensional modeling, metric governance, KPI dashboards, and turning data into decisions. How I like to start: a fixed-price pilot on your own data (usually 1 to 2 weeks). I connect one source, model your top KPIs, and deliver one dashboard plus a working chat-with-data copilot, so you see the result before committing to a full build. If you want analytics your whole team can trust and actually use, send me a message and tell me about your data. I am happy to show a live demo of the copilot in action.

Updated on [24/08/26] I build ETL and ELT pipelines on BigQuery, Snowflake, and PostgreSQL using Python, SQL, dbt, and Airflow, then turn them into Power BI and Tableau reporting the business can trust. 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 spend my time, and the chart at the end is the easy part. What I work with: Pipelines and orchestration: Airflow, Mage AI, dbt, Python, SQL, PySpark Warehouses: BigQuery, Snowflake, PostgreSQL, ClickHouse Streaming: Kafka, PyFlink, event-time processing Cloud: AWS Lambda, S3, EventBridge, SNS, Docker, FastAPI Reporting: Power BI, Tableau AI and LLM: RAG systems, Qdrant, Ollama, local model workflows How I build: Transformations get structured bronze through gold as dbt models, with schema tests and business rule checks written into the models themselves, so a broken assumption gets caught in the pipeline instead of by whoever opens the report next. Bad records go to a quarantine bucket rather than sitting quietly in a table someone trusts. Failures alert instead of failing silently.Reporting comes after the data is solid. I build around the one question the business needs answered, not a stack of generic rollups nobody reads. When the need is document search 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. Recent work: 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. A serverless AWS pipeline where a quarantine bucket keeps bad files from ever reaching the tables people query. Currently at Genix Pharma building AI-assisted workflows on local LLMs through Ollama for model experimentation and automated reporting. 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, and 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