Hire the Best Data Engineers
in Nigeria

I design and build robust data pipelines that turn raw data into clean, actionable insights. I specialize in automating ETL workflows, integrating multiple data sources, and ensuring systems are reliable and scalable. My focus is on practical, well-engineered solutions that work in real-world scenarios and grow with your business. How I work: Analytical and quality-focused: I create systems that minimize errors and require minimal maintenance. Transparent communication: You’ll always know the status of your project, and I provide feedback quickly. Honest guidance: If a requirement doesn’t make sense, I’ll point it out and suggest better alternatives. Core skills: Python & SQL: Efficient, maintainable, and clean code. ETL/ELT & Data Pipelines: Python, DBT, Dagster, Databricks (PySpark), Airbyte. Data Cleaning & Transformation: Turning messy data into reliable, analytics-ready formats. Cloud & DevOps: AWS (Lambda, Glue, Athena, S3, EC2, Batch, Step Functions), Docker, ECS, Bash scripting, CI/CD (GitHub Actions, CodePipeline), Terraform. I deliver solutions that are accurate, scalable, and tailored to your needs. My goal is simple: make your data work for you, not the other way around.

Need data, but do not know how to get it? Have data, but no reliable system running it?; OR Need to automate your workflow, but stuck in AI hype instead of real solutions? I help clients and businesses solve three core data problems: getting data, running data, and improving workflows with automation. My work includes Python development, web scraping, browser automation, API integrations, ETL pipeline design, cloud-based data systems, and AI workflow integration. I build solutions using tools such as Selenium, Playwright, Requests, Docker, Terraform, AWS, BigQuery, Microsoft Fabric, Databricks, and dbt. I am also AWS Certified, and I focus on building systems that are reliable, maintainable, and aligned with real business needs — not scripts that break after one run.

A resourceful and data-driven professional with strong interpersonal skills and hands-on experience in Data Analysis, Data Engineering, Visualization, Machine learning, Object-Oriented Programming, and Data structure and algorithm. Skilled in applying data analysis, data science, data engineering, and machine learning techniques such as statistical analysis, classification, regression models, building and deploying data pipelines, managing data warehouse and cloud infrastructures (GCP), and data visualization to generate insight to solve business problems cutting across growth, marketing, and product teams. Framework tools: Python, MongoDB, MySQL, BigQuery, SnowFlake, GCP, DBT, FiveTran, Metabase, Mixpanel, Power BI, Tableau, Google Data Studio, Apache Airflow, Amazon WorkSpace Therefore, feel free to contact me anytime to help in the analysis of your data and get the best quality of work Thanks.

I extract data from the web at scale, build AI-powered applications, and engineer the pipelines that connect them — from raw scrape to clean, queryable insight. 🕷️ Web Scraping & Data Extraction (Core Specialty) Whether it's a single site or a fleet of 30+ scrapers running in production, I build extraction systems that don't break: Stealth Automation: Bypass Cloudflare, CAPTCHA, Akamai, DataDome, and rate limiting using Playwright, Selenium, Puppeteer, and Scrapy — with proxy rotation and fingerprint spoofing Complex Targets: Facebook, TikTok, LinkedIn, Shopify, HubSpot, QuickBooks, SaaS platforms, paywalled sites, and JS-heavy SPAs Scale: Multi-million row pipelines with scheduled runs, error monitoring, and auto-recovery Output: Structured CSV, JSON, PostgreSQL, BigQuery, or direct API feeds — whatever your stack needs Experience: 5+ years maintaining large-scale scraping systems with real anti-bot handling — not just toy projects 💬 AI Web & App Development (Full-Stack) Custom AI chatbots and intelligent web applications for SMBs: AI Chatbots: Personalized customer service bots, knowledge bases, and conversational AI using LangChain, LangGraph, and RAG AI Agents: Autonomous agents with tool calling, multi-step reasoning, and decision-making Multi-Agent Systems: Agent-to-agent communication for complex workflows and collaborative problem-solving Personalized AI for SMBs: Custom solutions trained on your business data and documents Frontend: Next.js, React.js, TypeScript with modern, responsive UI design Backend: FastAPI, Flask, Django, Node.js Express for high-performance APIs Vector Databases: Pinecone, Milvus, ChromaDB for semantic search Deployment: AWS, GCP, Azure, Digital Ocean with Docker and Kubernetes ⚡ Data Engineering & ETL Pipelines Production infrastructure processing millions of records: Pipelines: Apache Spark, Airflow, Dagster for automated ETL Streaming: Kafka for real-time, event-driven systems Batch Processing: AWS Glue, Google Dataflow, Azure Data Factory Warehouses: BigQuery, Redshift, Snowflake with optimized schemas Transformations: DBT for data quality and business logic Migration: Zero-downtime moves to cloud warehouses 🤖 AI & LLM Integration Frameworks: LangChain, LangGraph agents and workflows Models: Claude (Anthropic), GPT-4, DeepSeek, Llama 3, Hugging Face RAG: Vector embeddings and semantic search for intelligent retrieval Observability: LangSmith and Langfuse for monitoring APIs: AWS Bedrock, Azure OpenAI, Anthropic Claude Fine-tuning: Custom model training for domain-specific tasks ☁️ Cloud & DevOps AWS: Lambda, ECS Fargate, Glue, Athena, Redshift, EventBridge, SNS, SQS, EMR GCP: BigQuery, Cloud Run, Dataflow, Dataproc, Firestore Azure: Data Factory, Databricks, Blob Storage, Cosmos DB DevOps: Docker, Kubernetes, Terraform, GitHub Actions, GitLab CI/CD Monitoring: CloudWatch, Datadog, Prometheus, Grafana 💻 Tech Stack Python · SQL · JavaScript · TypeScript · Bash · FastAPI · Next.js · React · PostgreSQL · MongoDB · Redis · BigQuery · Scrapy · Playwright · Selenium · LangChain · Docker · Kubernetes · AWS · GCP · Azure ✅ Why Work With Me I've built and maintained scraping systems at real scale — not just demos AI-assisted development means 2–3x faster delivery without cutting corners Full-stack ownership: frontend, backend, data, and deployment 100% Job Success Score on Upwork, Top Rated Plus Clear communication, regular updates, no surprises

Top Rated Data Engineer specializing in ETL/ELT pipelines, backend systems, web scraping, API development, and cloud data infrastructure across AWS, GCP, and Azure. I help companies build reliable data systems that reduce manual work, improve operational efficiency, and make data infrastructure easier to maintain over time. My focus is not only on getting pipelines working, but on making sure the full system is documented, reusable, scalable, and dependable for the team that inherits it. What I help clients with: - Data architecture design for clean, maintainable systems - ETL and ELT pipelines using SQL, Python, PySpark, DBT, and Polars - Web scraping and extraction workflows built for quality and scale - Backend API development with FastAPI, Python, and serverless patterns - Cloud data workflows across AWS, GCP, and Azure - Process documentation and workflow design for long-term team efficiency - AI-enabled data and automation workflows using OpenAI and LangChain How I work: - I prioritize reliability, documentation, and clean abstractions - I design systems with maintainability and reuse in mind - I communicate clearly throughout delivery so technical decisions stay aligned with business needs Projects I am best suited for: - teams that need long-term data infrastructure, not one-off scripts - pipeline modernization and backend cleanup - scraping systems that need better quality, resilience, and storage workflows - API and cloud implementations that support downstream analytics or product use cases What clients consistently value: - strong technical ownership - thoughtful backend and data architecture decisions - clean organization and maintainable code - dependable collaboration and proactive problem solving Selected client feedback: 🟢 "Timothy was an absolute pleasure to work alongside. He's really talented when it comes to both implementing and scaling automated scraping solutions. We had more or less a semi-functional system that he not only helped improve from his knowledge of backend and web, he also went above and beyond to recommend ways to maintain quality for the entire system from extraction to storage and everything in between. Thanks for the amazing work Timothy!" 🟢 "Timothy is a very diligent freelancer and a talented data engineer. He is available any time we need his help. He builds the database and data pipelines for our business and maintains the backend structure very clean and organized. Very good job!" 🟢 "Thanks for stepping in and mentoring out our team! I got positive feedback from my engineer on your expertise and knowledge in not only building solid data connections but also in appropriate levels of code abstraction to make the system (backend) run more smoothly. Thanks again!" 🟢 "Timothy was a pleasure to work with. He has great technical skills in AWS Glue, Athena, and other services. I would highly recommend working with him. He responded in a timely manner and has great enthusiasm for his work!" If you need help improving data operations, building robust data systems, or developing efficient pipelines and APIs, I’d be glad to discuss your project and suggest the most practical path forward.

I build production-grade data pipelines for location data, the kind that survive real scale, messy sources, and senior technical review. Most data engineers can't work in GIS, and most GIS specialists can't ship reliable pipelines. I do both, which is exactly what location-heavy projects need. If you're working with geographic data, satellite or aerial imagery, parcel and infrastructure records, point-of-interest datasets, anything tied to coordinates, I turn it into clean, queryable, automated systems instead of one-off scripts that break the moment the source changes. What I do: - Geospatial ETL pipelines (PostGIS, geopandas, rasterio) that ingest, clean, and structure spatial data at scale - Remote-sensing and imagery workflows, sourcing, tiling, and organizing aerial/satellite data across many locations - Web scraping and data extraction (Playwright, BeautifulSoup) with proper deduplication, rate-limit handling, and validation - Config-driven, schema-adaptive pipelines that adapt to new sources without a rewrite - Spatial APIs and mapping backends (FastAPI, PostgreSQL/PostGIS, Leaflet) - AI-connected spatial systems (MCP servers that expose PostGIS queries and geospatial tools to AI assistants) How I work: I diagnose the real bottleneck before writing code, I'm honest about tradeoffs (including when the hard part is data cost or source limits, not engineering), and I build for handoff, documented, maintainable, and yours. Proof: - Processed 6M+ US building permit records through a config-driven ETL pipeline (ConstructIQ) - Built a nationwide vendor directory of 4,400+ records via multi-phase scraping and enrichment (EventStarted) - Mapped fiber infrastructure across Lagos State in PostGIS (UDIGAP) - Built hybrid MCP servers exposing PostGIS and standalone geospatial tools to AI assistants, published to Glama (geo-mcp) - B.Sc. in Surveying & Geoinformatics — the geospatial fundamentals behind the engineering Tell me what your data looks like and what you need out of it, and I'll map the approach against your constraints before we talk price.