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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.

I am a Software Engineer with over a decade of experience in Backend development, CRUD, Web, RESTful APIs, Unit Testing, and Scala. I am dedicated to creating scalable applications that help my clients stand out. I am currently seeking challenging projects and opportunities to utilize my problem-solving skills and experience to help businesses achieve their goals in both technology and business domains. If you value well-designed, impactful software systems delivered efficiently, I am the right person for you. Technologies and platforms I am proficient in include Scala, Play Framework, Akka, RDBMS, NoSQL, ZIO, HTTP4S, Cats Effect, Monix, Kafka, AWS, Apache Spark, RAG, Gen AI, LLMs, Claude, Codex, and Functional Programming.

I design and build production-grade data and machine learning systems for financial services, insurance, and risk-heavy environments where correctness, auditability, and scalability matter more than experimentation. My focus is not on isolated models or dashboards — but on end-to-end data systems that transform raw operational data into governed, decision-ready intelligence used for risk detection, anomaly identification, and automated decision support. I have worked on insurance and financial data systems involving anomaly detection, claims intelligence, and structured risk scoring pipelines, including the operationalization of ML systems within real production environments using AWS and distributed data frameworks. A key part of my work includes building governance-heavy architectures where every stage of the pipeline — ingestion, validation, transformation, modeling, and output — is controlled, traceable, and production-safe for financial decision-making. Core Expertise Production Data Engineering - AWS (Lambda, Glue, S3, Athena, CloudFormation) - Apache Spark / PySpark distributed processing - SQL optimization and data modeling - Streaming and batch pipeline architecture Machine Learning Systems - Anomaly detection for insurance and financial datasets - Risk scoring and classification systems - Model operationalization (not just training, but deployment logic) - Data quality and feature governance for ML pipelines Financial & Insurance Data Systems - Claims data modeling and validation - Policy and customer data consistency checks - Fraud / anomaly detection systems in structured datasets - Risk signal generation for downstream decision systems - Actuarial ETL pipeline that measures how policyholder behavior (lapses, deaths, disabilities, etc.) Governance & Architecture - Financial scoring system design - Data validation gates and auditability layers - Production-safe ML pipelines with monitoring considerations - Scalable ETL/ELT frameworks for regulated environments Selected Experience Highlights - Built an ML-driven anomaly detection system for insurance motor claims, working closely with data science teams to operationalize models into production pipelines using AWS and Python. - Designed and built governance-heavy financial modeling systems focusing on structured risk scoring, validation rules, and audit-ready data pipelines for financial decision support. - Developed streaming and batch data pipelines using AWS services and Spark, including ingestion, transformation, and feature engineering layers for analytical and ML workloads. - Worked on AI and data-driven systems through Omdena collaborative projects, including recommender systems, RAG-based knowledge systems, and disaster preparedness analytics.

👋 Hi there, Businesses do not struggle because they lack data. They struggle because their data is scattered, unreliable, or difficult to turn into decisions. I help organizations build scalable data solutions that transform raw data into trusted insights for reporting, analytics, and business growth. Over the past 3+ years, I have worked across healthcare, real estate, marketing, finance, and technology sectors, designing data pipelines, developing reporting solutions, improving data quality, and enabling stakeholders to make faster, data-driven decisions. 🔑 What I Can Help You With 📈 Data Analytics & Business Intelligence • Power BI, Looker Studio, Tableau • KPI dashboards and executive reporting • Financial, operational, and performance analytics • Data storytelling and decision support ⚙️ Data Engineering • End-to-end ETL/ELT pipeline development • Apache Airflow, dbt, BigQuery, Redshift • Data transformation, modeling, and automation • API integrations and data ingestion workflows 🛡️ Data Governance & Quality • Data validation and reconciliation • Data quality monitoring and reporting • Documentation, data dictionaries, and governance practices • Standardized reporting and data management processes 💡 Recent Results ✔ Reduced reporting time by 40% through automation of executive reporting workflows. ✔ Improved data pipeline performance by 25% through ETL optimization and process redesign. ✔ Built Power BI dashboards that increased reporting efficiency and improved visibility into operational and financial performance. ✔ Developed healthcare and financial analytics solutions that transformed complex datasets into actionable insights for stakeholders. ✔ Designed scalable cloud-based data pipelines using BigQuery, Airflow, dbt, and Python for analytics and reporting. 🛠️ Technical Expertise SQL | Python | Power BI | Looker Studio | Tableau | Apache Airflow | dbt | BigQuery | Snowflake | PostgreSQL | MySQL | AWS | Azure | GCP | Excel | Data Modeling | ETL/ELT | Data Governance | Reltio | Collate | Snowflake Horizon 🌟 What Clients Appreciate About Working With Me • Results First – I focus on solving business problems, not just building technical solutions. • Clear Communication – I work effectively with both technical and non-technical stakeholders. • Ownership & Reliability – I take responsibility for projects and deliver well-documented, dependable solutions. • Long-Term Value – I build scalable systems that continue creating value long after delivery. 📩 If you need help building data pipelines, improving reporting, creating dashboards, or establishing reliable data processes, I would be happy to discuss your project.

In the last 6 years I've helped SaaS, fintech, and e-commerce companies go from broken spreadsheets and siloed data to pipelines that actually run and dashboards that actually get used. Most recent wins: → Compressed BI delivery by 75% at 1Touch (5–10 dashboards per sprint vs one per quarter) → Reduced reporting turnaround by 43% at FieldPro → Cut warehouse costs by 20% by refactoring dbt models from full-refresh to incremental at Optimetriks → Boosted e-commerce conversion by 37% at Mamap Life My stack: Python · SQL · dbt · BigQuery · Snowflake · Apache Airflow · Airbyte · Looker Studio · Tableau · Power BI · Docker · GCP · AWS · Azure . Databricks I work fully remote and have done since 2020 — async delivery, clear communication, and no hand-holding required. If you need a pipeline built, a warehouse migrated, or a reporting layer that doesn't break every time the data changes, let's talk.

Top Rated freelancer with 100% Job Success — I build scraping systems that keep working after delivery, not scripts that break in a week. Recent example: I needed to extract Zillow property listings for a client building an AI deal-evaluation tool. Zillow's anti-bot stack catches most scrapers through fingerprinting and behavioral detection. Instead of fighting the rendered page, I drove the browser with nodriver — it talks to Chrome directly over CDP rather than through a WebDriver binary, which is the exact layer most detection scripts check for, so there's nothing to flag. I used that same CDP connection to intercept Zillow's own internal API responses before they ever hit the DOM, validated every record with Pydantic, and synced it straight into Supabase — 500+ listings per URL, under a minute, zero detections. That dataset now feeds an AI layer that scores whether a listing is actually a good deal. That's the kind of problem I like: not "can you scrape this," but "this blocks everyone — can you get through it cleanly and keep it running." What I do: 🔹 Extract data from any website — logins, dynamic content, protected APIs 🔹 Bypass anti-bot systems — Cloudflare, CAPTCHAs, fingerprinting, rate limits, CDP-level API interception 🔹 Scrape authenticated content — using real saved sessions to access dashboards and paywalled data like a logged-in user 🔹 Build production pipelines — validated, structured data delivered via API, CSV, database, or cloud storage 🔹 Set up monitoring — price tracking, stock alerts, change detection 🔹 Turn scraped data into AI-ready pipelines — RAG systems and decision layers built on clean, validated data Stack: Playwright, Selenium, nodriver, Scrapy, Crawlee, BeautifulSoup, Requests, Camoufox, UC ChromeDriver, curl_cffi, Docker, Pydantic, Supabase/Postgres Industries: E-commerce, real estate, job boards, social media, members-only platforms I send a clear plan before we start — what's possible, what's hard, and how I'll handle it. No surprises mid-project. Ready to start? Send me the website and the data you need — I'll review the target site's specific protections and come back with a clear scope, approach, and timeline before we begin.