Hire the Best IBM InfoSphere DataStage Specialists

I help companies go from scattered data and inconsistent reporting to a reliable, scalable analytics layer built on Google Cloud Platform, powered by dbt, and delivered through Looker. For long term projects, happy to offer a FREE 1 week period of work, let's call it probation period. My results are faster queries, trusted metrics, and dashboards that business teams actually rely on. Let's have a free 30 mins call to discuss your needs, possibilities and data challenge. I'm Peter , a Top Rated Plus (top 3%) Certified Data Engineer (+ Analytics Engineer) with over 9 years of experience transforming complex data into actionable insights for global clients and start ups across finance, hospitality, SAAS, eCommerce, and tech. I specialize in building scalable data solutions that reduce costs, accelerate decision-making, and unlock new revenue streams. My experience: - Worked with leading organizations like KPMG, Savings United, IU Group, aleno, Andela, and the World Bank to define, maintain and optimize their data solutions. - Cut data processing time and operational costs by 20 percent through optimized pipelines and optimized queries and data storage techniques. - Helped a hotel SaaS platform unlock a new revenue stream via client-facing analytics leading to a paid embedded dashboard revenue stream. - Built and tracked KPIs that improved business steering and day-to-day execution delivered timely, cheaply and reliably. Tech & Tools I work with: GCP (BigQuery, Cloud Functions, Cloud scheduler, Cloud Logging, IAM, Compute Engine, Billing and optimization), AWS, Looker (Semantic layer, LookML,Looker API, administration, migration, setup, training, costs, looker embed, pdts), Power BI, Alteryx, dbt, dlt, Python, SQL, REST APIs, Airbyte, Airflow, Docker Some of my Certifications: Project Management Professional (PMP) - Project Management Institute (PMI) Google Cloud Professional Data Engineer Responsible AI with Google Cloud Generative AI Fundamentals – Google What I Deliver: - Automated, scalable pipelines to replace error-prone manual processes - Insightful dashboards and metrics that fuel performance and decision-making - Machine learning integrations and fraud detection solutions - Clear, timely communication and documentation - Google Looker setup, development, optimization and administration - Collaboratively leading agile teams towards accurate, timely and impactful data solutions. Clients consistently rate my work 5 stars for delivering high-quality, high-impact data solutions with a collaborative mindset. Whether you need a short-term fix or a long-term data strategy partner, I bring enterprise-grade quality to the Upwork platform. Let’s connect and discuss how I can help you leverage your data for strategic advantage.

Messy data slowing your team down? I build scalable ETL/ELT pipelines and modern cloud architectures on Azure, Databricks, Fabric, and Snowflake that turn raw, chaotic data into clean, analytics-ready systems fast and reliably. I bridge the gap between fragmented data sources and production-grade dashboards, seamlessly adapting to your existing infrastructure rather than forcing an expensive rebuild. What I Can Help You With: Data Warehouse & Lakehouse Architecture: Implementing Medallion design patterns (Bronze → Silver → Gold) using Delta Lake, Microsoft Fabric OneLake, and Snowflake. Scalable ETL/ELT Ingestion: Building automated, metadata-driven pipelines via Azure Data Factory, Fabric Pipelines, Databricks (PySpark/SQL), and dbt. Real-Time Data Streaming: Architecting low-latency workflows using Apache Kafka, Azure Event Hubs, and streaming engines. Database Design & Optimization: Performance tuning, indexing, and data modeling for PostgreSQL, Azure SQL, and cloud warehouses. Proven Project Highlights: Microsoft Fabric Incremental Pipeline: Built a control-table pattern using Get Metadata, Lookup, and ForEach loops to orchestrate zero-duplicate, quarterly ingestion from SharePoint into OneLake via Dataflow Gen2. Azure/Databricks Streaming: Developed a restaurant analytics platform processing 80,000+ events/day, cutting reporting lag from 6 hours to under 3 minutes. Kafka/Snowflake Pipeline: Engineered a real-time stock market data pipeline tracking 120+ tickers with under 8 seconds end-to-end latency. I write clean, documented code your team can maintain long-term and provide transparent daily updates. Message me with your data challenge and I’ll walk you through exactly how to solve it.

I help businesses build reliable data pipelines, cloud infrastructure, and backend systems that scale. My core work includes Azure Data Factory pipelines, SQL data warehousing, Snowflakes, Terraform-based infrastructure, AWS/Azure deployments, and backend integrations for data-heavy applications. I focus on production-ready systems that are stable, observable, and built for real business use. I have worked on projects such as: • Building Azure-based ETL pipelines and warehouse processes integrating platforms like Shopify, NetSuite, UKG, Air1, and custom systems • Processing large daily data volumes with incremental and full-sync strategies • Designing SQL procedures, reconciliation workflows, and reporting pipelines for operational and executive dashboards • Automating cloud infrastructure and deployments using Terraform, AWS, Jenkins, Docker, and Kubernetes • Improving performance, reliability, and cost-efficiency in backend and AI-driven systems What I can help with: • ETL / ELT pipelines • Azure Data Factory workflows • Azure SQL / PostgreSQL / SQL optimization • Data warehouse design • Backend API integrations • Terraform infrastructure automation • AWS / Azure deployment workflows • Monitoring, logging, and production reliability improvements Why clients work with me: • I understand both data and backend systems, so I can solve integration problems end-to-end • I care about business outcomes, not just writing code • I communicate clearly and keep delivery practical • I build with maintainability and production use in mind If you need help with a data pipeline, warehouse workflow, backend integration, or cloud infrastructure setup, I’d be glad to discuss your project. Certifications: AWS Certified Solutions Architect HashiCorp Terraform Associate

🏆 TOP RATED PLUS || Top 1% on Upwork || 8+ Years of Experience || 100% Job Success || Expert Vetted Most data teams are held back by unreliable pipelines, untrustworthy warehouses, and data infrastructure never built to scale. That's exactly what I fix. As a Senior Data Engineer, I don't just write SQL and call it a pipeline. I architect end-to-end data systems where reliable ingestion feeds into clean, versioned transformations that power decisions your business can act on. My approach prioritizes fault tolerance, scalability, and observability across both batch processing and real-time analytics workloads. This ensures your data infrastructure is not just functional, but resilient and audit-ready. Whether you need cloud data migration, data platform modernization to a Modern Data Stack (Snowflake/dbt/Airflow, Microsoft Fabric), or streaming analytics infrastructure, I deliver production-grade systems that help technical founders and data teams eliminate pipeline debt, automate complex data workflows, and build scalable infrastructure ready for AI workloads. ------------------------ Where I make the biggest impact: ✅ I lead data migration and data platform modernization projects, replacing brittle ETL and ELT pipelines with a Modern Data Stack built on Snowflake, dbt, Airflow, and Microsoft Fabric. ✅ Every engagement includes Medallion Architecture design, full test coverage, CI/CD for data models, data lineage tracking, and documentation that outlasts the project. ✅ I design data pipelines for both batch processing and real-time analytics, idempotent, schema-drift tolerant, and monitored through data observability frameworks, so failures are caught before they reach your stakeholders. ✅ Warehouse models are built to serve the business: Star Schema, dimensional modeling, dbt projects, analytics engineering best practices, and a metrics layer backed by a data catalog and metadata management. ✅ I architect distributed systems for big data and streaming analytics, including Kafka, Flink, Spark Structured Streaming, exactly-once semantics, dead-letter queues, and end-to-end latency guarantees. ✅ AI data pipelines are engineered to feed LLMs and ML systems with clean, structured, high-quality data, from ingestion through transformation to serving. ✅ I bring governance to data platforms through data mesh, data catalog implementation, metadata management, and data integration across systems. ✅ Data quality and data reliability are enforced end to end, with automated frameworks, SLA monitoring, auditable lineage, and observability that catches bad data before it reaches your stakeholders. ✅ I build AI-ready data infrastructure and lakehouse foundations, Delta Lake, Apache Iceberg, cloud data architecture, and CDC pipelines for near-real-time sync. ✅ Cloud data migration is handled end to end, from legacy warehouse assessment through cutover, with zero data loss and minimal downtime. ------------------------ What I Build With: 🗄️ Warehouses, Lakehouses & Data Lakes: Snowflake, BigQuery, Redshift, Databricks, Microsoft Fabric, Delta Lake, Iceberg ⚙️ Transformation: dbt (Core & Cloud), SQLMesh, Spark, PySpark, Star Schema, Medallion Architecture 🔁 Orchestration: Airflow, Dagster, Prefect, Azure Data Factory, Microsoft Fabric 📨 Streaming: Kafka, Kinesis, Pub/Sub, Flink, Fabric Eventstream, ClickHouse 🔗 Ingestion: Fivetran, Airbyte, Matillion, Stitch, Hevo, Meltano, CDC pipelines ☁️ Cloud: AWS, GCP, Azure 🐍 Languages: Python, SQL (SF, BQ, T-SQL, PL/pgSQL), FastAPI 🗃️ Databases: PostgreSQL, MySQL, SQL Server, DynamoDB, MongoDB 📊 BI & Reporting: Looker, Tableau, Power BI, GA4, Metabase, Superset, Streamlit, Grafana ------------------------ What Clients Say: ⭐ "Adarsh rebuilt our analytics pipeline on Snowflake, Airflow, and dbt, giving us reliable, version-ready data. Reporting accuracy improved overnight, and we can finally trust the numbers." – Anita, Head of Product, FinTech SaaS ⭐ "He designed a zero-downtime migration to a modern data warehouse that cut query latency by more than half while keeping our SLAs intact." – Daniel, VP of Data, AdTech Firm ⭐ "Adarsh built our entire data platform from the ground up. Clean architecture, solid dbt models, and Airflow pipelines that have been running without issues for months. He brought a level of engineering discipline we hadn't seen from a data consultant before." – Mark, Director of Data Engineering, E-commerce Startup ⭐ "We came to Adarsh with a Spark pipeline that was costing us a fortune and delivering stale data. He identified the bottlenecks, restructured the workflow logic, and reduced our processing time by 70%. Technically sharp, communicates clearly, and delivers without hand-holding." – Leo, Head of Analytics, HealthTech SaaS ------------------------ 🚀 Let's Build Your Data Foundation. If your data infrastructure needs to be faster, cleaner, and trustworthy, send a quick message about your project, and I'll take it from there.

𝐈 𝐛𝐮𝐢𝐥𝐝 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 𝐟𝐨𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐝𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐫𝐨𝐤𝐞𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬, 𝐬𝐜𝐚𝐭𝐭𝐞𝐫𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐬𝐥𝐨𝐰 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐮𝐧𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐦𝐞𝐭𝐫𝐢𝐜𝐬. I’m a Senior Data Engineer with 10+ years of experience building cloud data platforms, ETL/ELT pipelines, lakehouses, warehouses, and analytics-ready data layers using Microsoft Fabric, Snowflake, AWS, BigQuery, dbt, Python, SQL, Airflow, Fivetran, Airbyte, and Databricks. My focus is not just moving data from point A to point B. I design reliable data systems that are automated, scalable, well-modeled, and trusted by business teams. 𝐖𝐡𝐚𝐭 𝐈 𝐡𝐞𝐥𝐩 𝐰𝐢𝐭𝐡 ✅ 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Lakehouse, Warehouse, Dataflows Gen2, pipelines, notebooks, semantic models, Medallion architecture, and Power BI-ready data layers. ✅ 𝐂𝐥𝐨𝐮𝐝 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, SQL Server, and Azure Synapse architecture. ✅ 𝐄𝐓𝐋/𝐄𝐋𝐓 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 API ingestion, database replication, SaaS integrations, file ingestion, batch jobs, incremental loads, and scheduled workflows. ✅ 𝐝𝐛𝐭 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 Staging, intermediate, marts, incremental models, tests, documentation, metric definitions, and business logic standardization. ✅ 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 & 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 Airflow, Dagster, AWS Lambda, Glue, Step Functions, ADF, CI/CD, monitoring, retries, alerts, and production workflow automation. ✅ 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 & 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 Deduplication, reconciliation, schema drift handling, validation rules, MDM, Golden Record logic, RBAC, access control, and audit-ready reporting layers. 𝐑𝐞𝐜𝐞𝐧𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 Built a centralized Fabric platform with Lakehouse, Warehouse, Dataflows, pipelines, semantic models, and Power BI reporting layers for a global organization with fragmented reporting sources. 𝐍𝐞𝐚𝐫 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 Designed operational pipelines into Snowflake with incremental ingestion, schema change handling, deduplication, retries, and reliable dbt-based reporting models. 𝐌𝐨𝐝𝐞𝐫𝐧 𝐒𝐚𝐚𝐒 𝐝𝐚𝐭𝐚 𝐬𝐭𝐚𝐜𝐤 Centralized HubSpot, Stripe, GA4, Google Ads, Salesforce, MongoDB, and product data into BigQuery/Snowflake using Fivetran, Airbyte, dbt, Dagster, and Metabase. 𝐋𝐞𝐠𝐚𝐜𝐲 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐜𝐥𝐨𝐮𝐝 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 Led migrations from SQL Server, SAP BW, Oracle, Hadoop, and on-prem systems into modern cloud warehouses with optimized performance and automated workflows. 𝐀𝐖𝐒 𝐝𝐚𝐭𝐚 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 Built automated workflows using S3, Lambda, Glue, Step Functions, IAM, SNS, CloudWatch, and Python to reduce manual reporting and improve pipeline reliability. 𝐓𝐨𝐨𝐥𝐬 𝐈 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 Microsoft Fabric, Snowflake, BigQuery, Redshift, Databricks, Azure Synapse, PostgreSQL, SQL Server, Oracle, SAP BW, Hadoop, dbt, Python, SQL, Airflow, Dagster, Fivetran, Airbyte, ADF, SSIS, Talend, AWS S3, Lambda, Glue, Step Functions, IAM, SNS, CloudWatch, Power BI, Tableau, Metabase, and Looker. You should reach out if you need a senior data engineer to: ✅ Build a cloud data warehouse or lakehouse ✅ Migrate legacy systems to Snowflake, Fabric, BigQuery, or AWS ✅ Fix unreliable ETL/ELT pipelines ✅ Design dbt models and trusted reporting layers ✅ Automate manual reporting workflows ✅ Integrate APIs, CRMs, ERPs, databases, and SaaS platforms ✅ Build production-ready data infrastructure for analytics and BI If your data stack is messy, slow, or hard to trust, send me a message. I’ll help you map the cleanest path from scattered systems to a reliable data platform.

I build data platforms that work at scale and keep working as your business grows. Over the past 10 years I've served as the lead or founding data engineer across fintech, e-commerce, ride-hail, legal tech, and cybersecurity companies. That means I've designed systems from scratch, made architecture decisions with no one to fall back on, and delivered platforms that product teams actually use. Here's what I typically get hired to do: → Build greenfield data platforms on AWS or GCP from the ground up → Design and ship production ETL/ELT pipelines (Airflow, Dagster, dbt) → Set up scalable warehouses and governance (Snowflake, BigQuery, Redshift) → Implement real-time streaming pipelines (Kafka, Spark Streaming, CDC) → Build AI-powered data applications (RAG, LLMs, LangChain, vector DBs) → Fix broken or unreliable pipelines and make them production-grade → Architect cloud infrastructure on AWS, GCP, Azure (Terraform, Kubernetes) Recent work includes: - Led data platform engineering for a US e-commerce company processing billions of events daily. I re-architected ingestion pipelines, built Snowflake governance from scratch, introduced Prometheus monitoring and CI/CD standards across the platform. - Built a full data platform on GCP (BigQuery, Dataproc, Airflow) for a music streaming company. Firebase, AppsFlyer, and app store data all flowing into one warehouse within weeks. - Designed an AWS data platform for a ride-hail company managing 500+ streaming and 700+ batch jobs — including a self-serve portal that replaced multi-step CLI workflows for engineers. - Built a legal AI search engine using LangChain, Pinecone, and RAG — full pipeline from document ingestion to LLM-generated answers, deployed on AWS with auto-scaling. - Built an AI inventory insights agent for a US automotive company — multi-source data pipelines, real-time APIs, conversational interface. I work in English daily, communicate proactively, and deliver production- ready code — not prototypes. I'm used to working directly with CTOs and technical leads in US and European time zones. Tools I work with regularly: Python · SQL · Airflow · Dagster · dbt · Snowflake · BigQuery · Spark · Meltano · Kafka · AWS (S3, EMR, Glue, ECS, Lambda, EC2, EKS) · Databricks · GCP · Azure Terraform · Docker · Kubernetes · LangChain · FastAPI · MLflow · Weaviate, Celery If you're building a data platform, fixing one, or adding AI/ML capabilities to your stack, let's talk.