Senior Data Pipeline Engineer Needed

@Shrutibimpl

You want an extensible, production grade data pipeline that automates MySQL extraction, routes clean data to your analytics layer, and scales gracefully as you add new sources. This data infrastructure ensures your analytics team always works with accurate, fresh daily tables instead of querying slow production databases. By implementing automated runbooks, automated alerts, and self healing recovery steps, you get peace of mind that pipeline failures are resolved before they impact morning business reporting. Your team gains a trusted foundation for making decisions without having to waste engineering hours manually rebuilding broken data dumps. We will build this pipeline using Apache Airflow and dbt with Python to separate extraction from transformation, allowing modular scaling for future API or CSV inputs. We will write Terraform scripts to provision the cloud infrastructure and implement Pytest for unit testing your SQL transformations. The jobs will run daily, leveraging Airflow Slack integration for immediate alerting, while tracking pipeline runs through a structured schema designed to support easy backfills.

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. Having spent more than seven years in the data engineering field, I can assure you I possess the experience and comprehensive understanding needed to own this project. I have a proven track record of designing and building high-quality, robust data pipelines that automate ETL processes whilst maintaining reliability and scalability. When it comes to tooling, while Python and Airflow are undeniably strong contenders for the task at hand, my skill set covers a wide range of languages and frameworks that can be leveraged as per your specific needs. My focus is not only on delivering functional code - I incorporate meticulous unit and integration testing to ensure the accuracy and stability of critical transformations. In terms of delivery, I'm well-versed with Git repository management, making sure adequate documentation is provided so that any engineer on your team can seamlessly understand and extend the workflow.

@haitham1996

Hi,I’m a Software Engineer Udacity certified in Full Stack Web Dev and Data Analysis track with over 4+ years of experience building scalable backend systems, RESTful APIs, and automation solutions using these tracks including Java (Spring Boot), Python/Django, and modern low-code tools like N8N. I focus on turning complex requirements into efficient, reliable systems that save time and drive real results. With experience across enterprise, freelance, and self-driven projects, I bring strong problem-solving skills, adaptability, and a results-oriented mindset to every project. I’d be glad to connect and explore how I can add value to your team or business. Check my profile : https://www.freelancer.com/u/haitham1996?frm=haitham1996&sb=t

@zain556

Hi, This aligns perfectly with my background. As an Architect specializing in high-concurrency data infrastructure, I build resilient pipelines optimized for zero data loss and future scalability. Tech Stack & Extensibility Orchestration: Apache Airflow (Python) for modular DAGs, auto-recovery, and alert monitoring. Ingestion & Load: Incremental MySQL tracking to prevent database strain. Built with an abstracted layer so adding APIs or CSVs later won't require a rewrite. Transformations: dbt (Data Build Tool) for version-controlled SQL, data lineage, and schema testing. Infrastructure: Terraform (IaC) for identical, instant staging and production deployment. Relevant Experience I have engineered multiple enterprise data pipelines, including multi-source ingestion layers deployed on AWS processing millions of rows daily, and custom sync engines with automated failure alerting. Hard Deliverables Design Doc: Schema mapping, idempotency, and recovery strategy. Codebase: Modular ETL code with comprehensive unit/integration tests in Git. IaC: Terraform scripts for one-click environment replication. Run-Book: A concise Markdown guide for your team to easily manage the workflow. I am available to start immediately. Let’s jump on a quick call to align on your target warehouse so I can finalize the design. Best regards, Zain Hassan AutomexaSolutions

@sjohn117

Hi there, I’m Sean, a seasoned Data Pipeline Engineer with over 10 years of experience in data integration and ETL processes. I understand you need a reliable and flexible data pipeline to extract, transform, and load data from your MySQL database into your analytics layer while maintaining the ability to scale for future data sources. In a recent project, I developed a production-grade data pipeline utilizing Python and Airflow, ensuring automated daily data loads with robust monitoring and alerting mechanisms. My implementation optimized data extraction and transformation processes, enhancing data reliability and visibility in BI tools. I will create a thorough technical design outlining the tables, job orchestration, and a solid recovery strategy, ensuring both clean code and extensive testing for reliability. I anticipate delivering the first working milestone within one week, getting us on track for successful loads into your target warehouse. What is your current timeline for implementing the data pipeline and how do you plan to handle the integration of other data sources in the future? Thanks, Sean

@alluankit01

✨ I can build the MySQL to analytics data pipeline with a production ready structure, daily automation, monitoring, tests, and a clear run book for your team. I would start by reviewing the source MySQL schema, target warehouse, BI needs, table volume, and transformation rules, then prepare a technical design covering extraction, staging, transformations, scheduling, retries, and recovery. My preferred stack would be Python with Airflow or a lighter scheduler depending on your environment, with modular connectors so APIs or CSV sources can be added later without rewriting the pipeline. I have strong experience with Python, MySQL, PostgreSQL, ETL workflows, data cleaning, orchestration, testing, Git based delivery, and deployment scripts. I will include unit and integration tests for critical transformations, logging and alerts for failure points, and a concise run book so any engineer can operate or extend the workflow. Please share the MySQL table list, target warehouse details, and one sample BI output, and I can map the pipeline design before implementation. Best regards Ankit

@Lazarone1

Hi, I read your project carefully. I can build a modular ETL pipeline using Python and MySQL with structured transformation layers, monitoring, and future-ready source integration support. • Develop automated extraction and transformation workflows from MySQL with scheduled daily processing and recovery handling • Build modular pipeline architecture so APIs or CSV-based sources can be added later without rewriting the core workflow • Implement unit/integration tests, structured logging, and monitoring to detect failed jobs and data inconsistencies quickly • Deliver deployment scripts, Git-ready project structure, and concise documentation for staging and production environments I can deliver a working MVP quickly and refine the pipeline structure based on your analytics workflow. I have completed 10+ Python automation and database integration projects involving ETL workflows and API-connected systems. Question 1: Which target warehouse or BI tool will receive the transformed tables? Question 2: Do you prefer batch-only daily syncs or incremental loading support from the beginning? Let me know your answers. I can start right away.

@steviem3

⭐⭐⭐⭐⭐ ✅Hello, I’ve designed and owned production-grade ETL/ELT pipelines across MySQL-backed systems with scalable warehouse delivery, so I clearly understand the need for reliable orchestration, clean transformation layers, and extensible architecture that can grow beyond a single data source. In my previous work, I’ve built multi-source data pipelines using Python (Airflow, Prefect, and custom schedulers) that extract from transactional MySQL systems, apply structured transformation layers, and load into analytics warehouses such as PostgreSQL, BigQuery, and Snowflake. These systems included retry-safe job orchestration, incremental loading strategies, schema evolution handling, and full observability through logging, alerting, and failure recovery mechanisms. For this project, I will design a modular ETL architecture starting with MySQL ingestion, structured transformation layers, and a clean abstraction so additional sources (APIs, CSV, etc.) can be added without redesign. I’ll implement scheduled orchestration (Airflow or equivalent), monitoring hooks, and idempotent pipelines to ensure safe re-runs. The deliverables will include full code in your Git repo, infrastructure-as-code deployment scripts, unit/integration tests, and a clear run-book so your team can operate and extend the system confidently. Let’s connect so I can review your current schema and define the most efficient, scalable pipeline design for production use.

@kamalsehgal7000

I am committed to completing this project milestone with high accuracy, professionalism, and attention to detail. The work will be completed according to your requirements and delivered within the given timeline. I will ensure proper formatting, quality output, and smooth communication throughout the project. Any necessary revisions or improvements will also be handled to make sure the final result meets your expectations. My focus is to provide reliable, efficient, and professional service with complete client satisfaction.

@rakeshk322

Hi, I am a senior data engineer with 7+ years of experience building scalable ETL/ELT pipelines for analytics and reporting platforms. I’ve designed production-grade workflows that extract from MySQL, transform data reliably, and load into warehouses like BigQuery, Snowflake, and PostgreSQL with automated monitoring, retries, and alerting. I can deliver the technical design, tested ETL code, CI-ready deployment scripts, and a concise operational runbook aligned with your acceptance criteria. A few questions: • Which target warehouse and BI tool are you currently using? • Do you prefer batch-only processing or future support for near real-time ingestion? • Is the infrastructure already hosted on AWS, GCP, or Azure?

@ToufeeqX

I HAVE DONE SOMETHING SIMILAR BEFORE! Your need for a clean, professional, and user-friendly data pipeline that is seamlessly integrated and automated aligns perfectly with my experience. I specialize in building scalable ETL/ELT pipelines using Python and Airflow, ensuring robust monitoring and alerting mechanisms for failure points. My approach guarantees flexible architecture that supports future data sources like APIs and CSVs without major rewrites. You won’t find someone more aligned with what you’re looking for. While I’m new to Freelancer, my current priority is building strong reviews and long-term client relationships, so you’ll receive serious effort and high-quality work at a much lower rate. Come chat with me, worst case you get a free consultation :) Regards, Toufeeq

@adarshk6696

Hi, this side Adarsh here and i am currently working on data science project and ai agentic project, i think i handle your project efficiently, so if you give me proposal , i will try my 100 percent and given to you within 6 days.

@Geraldwhitehead

Application Specialist / SQL Server Database Administrator / Database Systems Engineer Experienced SQL Server database professional with deep hands-on expertise in database administration, synchronization, performance-minded system design, application/database integration, and mission-critical data workflows. Strong background supporting real estate, property appraisal, tax, and enterprise data systems. Skilled in SQL Server architecture, T-SQL, data migration, backup/recovery concepts, database security, application troubleshooting, and building database-driven software solutions. Combines DBA experience with advanced programming knowledge in COBOL, C++, C#, VB.NET, Azure, and distributed storage systems.

@bilal0225

This is a good fit for a modular, production-grade ETL/ELT build. I can design and implement a daily MySQL extraction pipeline that produces clean analytics-ready tables, with clear orchestration, retries, monitoring, alerting, and recovery paths so your team can operate it confidently. My preferred approach would be Python with Airflow for orchestration, SQL-based transformations where appropriate, Git-managed code, unit/integration tests for critical logic, and deployment scripts or IaC to keep staging and production reproducible. I’d structure the ingestion layer so future sources such as APIs or CSV drops can be added through reusable connectors rather than a rewrite. For the technical design, I would cover source-to-target mapping, table strategy, job dependencies, failure handling, backfills, logging, and run-book instructions. The final handoff would include working ETL/ELT code, tests, deployment assets, and documentation aligned with your acceptance criteria: green tests and a successful MySQL load visible in the BI layer. I can deliver a reliable, extensible pipeline that your engineers can maintain and expand. I’m available to

@dipjit11

With A industry experience of 12yrs and Knowledge in cloud as well as in ETL I believe it will be great fit to collaborate and work along. Just wanted to understand what is the volume of data to be integrated and is the data follows medallion architecture?? Lets connect for the same

@mrbhautik

You need a production-grade MySQL ETL pipeline that runs daily, handles failures gracefully, and is flexible enough to plug in APIs or CSV sources later without rewriting. I've built exactly this kind of end-to-end data pipeline in production. I build Python data pipelines, automated extraction systems, and structured data workflows including a Complete CRM Automation Pipeline (n8n, multiple data sources, scheduled automation) and an AI-Powered Database Chatbot (MySQL, FastAPI, structured data layer). Same disciplined architecture applies here: clean extraction, robust transformation, reliable loading, and full monitoring. My preferred stack for your pipeline: Python and SQLAlchemy — MySQL extraction, incremental and full load support Apache Airflow — daily job orchestration, retry logic, failure alerting PostgreSQL — target analytics warehouse Docker — deployment scripts for staging and production pytest — unit and integration tests on critical transformations What I'll deliver: Technical design — tables, orchestration, recovery strategy documented ETL code — Python and Airflow, committed to your Git repo Unit and integration tests — green test suite before handover Docker deployment scripts — spin up staging and production instantly Run-book — any engineer operates and extends independently What is your target analytics warehouse PostgreSQL, BigQuery, Snowflake, or Redshift? And do you have an existing Airflow setup or starting fresh?

@sujitgaikwad4608

Hello, I believe I’m a strong fit for this project because I have hands-on experience building scalable ETL/ELT pipelines, working with SQL, Python, cloud platforms, and production-grade data workflows. I have worked extensively on data engineering tasks involving automated ingestion, transformation, validation, monitoring, and reporting pipelines. For this project, I would design a modular and extensible architecture that initially integrates with MySQL while keeping the framework flexible for future API and CSV-based sources. My preferred stack would be Python, Airflow, SQL, and cloud-native orchestration tools, along with proper logging, retry handling, monitoring, and alerting mechanisms to ensure reliability in production. I can deliver: - End-to-end ETL/ELT pipeline implementation - Reusable and scalable architecture - Automated scheduling and orchestration - Unit and integration testing - Deployment scripts / IaC support - Clear documentation and operational runbook I also have strong experience with SQL optimization, data validation, analytics reporting layers, and Git-based development workflows. Recently, I have been working on enterprise-scale data workflows involving Synapse, Fabric, Spark, SQL pipelines, and analytics integrations, which aligns well with your requirements. I am available to start immediately and can collaborate closely with your team to ensure smooth delivery and production readiness. Looking forward to discussing the project further.

@ApieTech

Hi! Read the brief — production-grade MySQL → analytics warehouse pipeline, daily schedule, flexible enough to plug in API/CSV sources later without a rewrite. How we'd build it: Python + Airflow with separate Extract/Transform/Load DAGs so failures isolate cleanly. Source-system abstraction layer so MySQL today, REST/CSV/Postgres tomorrow without rewriting transforms. Idempotent loads with watermark tracking, retries with exponential backoff, and Slack/email alerts on failure. Tests with pytest + sample-data fixtures. Deployment via Docker Compose or Terraform to AWS MWAA, GCP Composer or self-hosted. Two related builds: a transactional MySQL → Snowflake pipeline for a fintech client with hourly watermarks, and a multi-source ETL into Postgres for an EdTech analytics dashboard. Both still running. Ping me with the target warehouse (Snowflake/BigQuery/Redshift/Postgres) and rough row volume — that'll let me sharpen the timeline.