Azure Data Factory Pipeline Developer

@KarthikTechCon

Azure Data Factory Specialist: With 15+ years of experience, I specialize in designing and optimizing Azure Data Factory (ADF) pipelines to meet intricate enterprise-scale integration and migration needs. I am ready to jump in immediately and tackle your project. Proposed Solution: - Design and implement linked services and parameterized datasets tailored to your SQL Server, Salesforce, and S3 data sources. - Develop and troubleshoot ADF pipelines with complex transformations to ensure accurate data processing. - Set up monitoring and alerting systems for proactive performance management and reliability. Key Deliverables: - Fully operational ADF pipelines with optimized SQL queries. - Comprehensive documentation detailing the pipeline design and transformation logic. - Performance tuning reports identifying and mitigating potential issues. Quality & Performance: - Ensure high availability and reliability of data pipelines under load. - Implement best practices for data integrity and security throughout the migration process. Timeline & Next Steps: - Estimated start within days, with key milestones achieved weekly for the next two months. - Regular check-ins to provide updates and address any challenges. Best Regards, Karthik B Resonite Tech

@Appworldz

Hi, I’m Sahanaj — Azure Data Factory is one of my daily weapons. I’ve built production ADF pipelines integrating SQL Server, Salesforce, APIs and S3, including parameterized datasets, complex transformations, optimized SQL, monitoring and alerting. I can jump in immediately, troubleshoot existing pipelines, and get the first production pipeline live this week. For the 2-month engagement, I’d suggest ₹70,000–₹75,000/month, depending on workload and pipeline complexity. I work independently, document properly, and keep updates short and useful. If reliability and performance matter more than just “getting pipelines running,” I’d be a strong fit.

@alluankit01

Hi, I can support your Azure Data Factory pipeline work immediately, including pipeline design, troubleshooting, optimization, API/database integrations, transformations, monitoring, and production deployment. The best solution is to first review your current Azure environment, data sources, target systems, linked services, datasets, pipeline standards, transformation rules, and monitoring requirements. Then I’ll build parameterized ADF pipelines for SQL Server, APIs, Salesforce, S3-style sources, and other systems, with clean transformation logic, tuned SQL, retry handling, logging, and alerting. I’m comfortable with Azure Data Factory, SQL Server, data migration, ETL/ELT pipelines, linked services, parameterized datasets, REST API integration, Salesforce/S3 data flows, data cleansing, performance tuning, monitoring, alerting, and production troubleshooting. Deliverables will include: * ADF pipeline development * Linked service setup * Parameterized datasets * SQL/API/Salesforce/S3 extraction * Rule-based transformations * Target system loading * Tuned SQL queries * Error handling and retries * Monitoring and alerts * Performance optimization * Documentation and handover notes * Short progress check-ins I’ll focus on building reliable, scalable, and production-ready ADF pipelines while proactively fixing performance issues and keeping you updated clearly throughout the engagement. Best regards Ankit

@jagratip3

Hi, I can jump in immediately and handle the full Azure Data Factory pipeline lifecycle, from architecture and development through troubleshooting, optimization, monitoring, and production support. I can build ADF pipelines connecting SQL Server, APIs, Salesforce, S3, and other data sources, with parameterized datasets, linked services, dynamic workflows, and complex rule-based transformations. I’ll also optimize SQL and pipeline execution to improve throughput, reduce failures, and keep large-scale migrations reliable. For production readiness, I can configure monitoring, logging, alerts, retry/error-handling logic, and clear documentation so the pipelines remain maintainable and easy to troubleshoot. I’m comfortable working independently, providing concise progress updates, and proactively identifying performance or reliability issues. I’m available to start immediately and can focus on getting the first production pipeline delivered quickly. Best regards, JP

@Prutheus

SQL Server and Salesforce and S3 into rule-driven transformations, owned end to end — the useful question is not whether the pipelines can be built but how quickly they can be debugged at 3am when a source changes shape without warning. How I work these: - Source contracts made explicit. Every extract validates the shape it expects and fails loudly on drift instead of quietly loading nulls into your target. - Transformations kept as declarative and parameter-driven as the rules allow, so a changed business rule is a config change rather than a pipeline rewrite. - Reconciliation built in: counts and control totals per run, with a summary that tells you the load was correct rather than merely finished. - Restartable and idempotent by default, so a failed run is a re-run and not a cleanup project. Background: I run Apache Airflow pipelines 24/7 for industrial clients, and I built a platform consolidating 14 separate data sources into one warehouse layer — including the messy ones with APIs that change unannounced. Strong SQL, Python, and cloud. Straight about ADF specifically: I use it as one orchestration tool among several rather than exclusively. If you need a pure ADF specialist with nothing else, say so and I will step aside. Available immediately for the two months. Question: how many pipelines are in flight right now, and which are broken? Martin

@shaun4014

I've recently helped a client achieve significant improvements in their data integration and migration processes using Azure Data Factory. This experience has equipped me with a deep understanding of the complexities involved in enterprise-scale initiatives. I will assist you in building, troubleshooting, and optimizing ADF pipelines that effectively extract data from various sources, including SQL Server and APIs like Salesforce, ensuring a clean and professional data flow. With a focus on developing linked services, parameterized datasets, and setting up robust monitoring and alerting systems, I bring a proven track record of delivering scalable and efficient ADF solutions. My portfolio showcases successful projects where I have tackled performance issues proactively while maintaining thorough documentation. Let's discuss how I can get started immediately and help you get the first pipeline into production this week. Regards, Shaun Kelly.

@DeanSaliegh

I can help you get your Azure Data Factory pipelines up and running efficiently. With your need for immediate support on enterprise-scale integration and data-migration initiatives, I can dive right in and start building, troubleshooting, and optimizing those pipelines. I noticed you're looking for someone to handle everything from linked services to performance monitoring. My approach involves not just getting the job done but ensuring reliability and scalability, which is crucial for your projects. I have done similar work: Modern Real Estate Investment Website ([link]). Let’s chat about your specific needs and the best approach. At the very least, you’ll get a free consultation. Regards, Dean

@ronakkataria1

Hi — "first pipeline into production this week" is the line I'd optimise for, so here's how to make that realistic rather than just agreeing to it. Week one: take the highest-value source (likely SQL Server, your simplest linked service), build it end to end with parameterised datasets from the start, and get it monitored. One production pipeline with alerting beats four half-built ones. How I work in ADF: - Parameterised datasets and linked services from day one, not retrofitted. Retrofitting parameterisation across 20 hard-coded pipelines is the biggest waste I see in ADF estates. - Salesforce and S3 are the sources that bite: Salesforce for API limits and incremental extraction via SystemModstamp, S3 for partitioning and credentials. I'd design incremental watermarking before volume forces it. - Performance starts with copy-activity DIU/parallelism and sink write behaviour — usually a bigger win than rewriting SQL, though I tune both. - Alerting on failure and on silent success with zero rows, which is the failure nobody catches. Questions: 1. Which targets — Synapse, Azure SQL, Snowflake, something else? 2. Self-hosted integration runtime needed for on-prem SQL Server? 3. Existing ADF estate to inherit, or greenfield? 4. Git-backed ADF with CI/CD, or authoring in the portal? Ronak — Azure and AWS certified architect; built a multi-cloud MLOps/data platform across Azure, AWS, GCP, Databricks and Snowflake.