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You will get Natural Language processing (NLP) Analytics and Model Building Expert
Project details
Advanced Natural language processing Analysis. I cover Bag of Words, Word Embeddings, Topic Modelling, Named Entity Recognition, Natural Language Generation, Text Mining, String Distance and Similarities, Fuzzy Matching, Sentiment Analysis, Text Summarization, etc. The models can be LDA, LSTM, Bagging, Boosting, etc. The tools can be Stanford NER, SpaCy, Scikit-Learn, TensorFlow, Gensim, etc
What's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 6 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 6 | 6 | 6 |
Number of Scenarios | 2 | 3 | 4 |
Number of Graphs/Charts | 5 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
30 reviews
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PM
Paul M.
Jun 23, 2022
He has done a great job. I have hired him five times for my projects. Excellent !
PM
Paul M.
Apr 12, 2022
He is committed to realising the works according to my particular requests. I have hired him several times because he knows what my projects were about. Thanks for all the works. Well done!
PM
Paul M.
Mar 24, 2022
He is committed to producing the god-quality works, and he knows what he is doing especially he is willing to customise my particular requests.
PM
Paul M.
Mar 12, 2022
He is agile, and is responsible. He has tried to achieve the goals of the project following my requests., He has tried to offer the extra miles. Thanks.
AA
Adnan A.
Aug 29, 2025
Need Data Engineer with expert level skills of Azure Delta Lake, Azure Data Bricks and Python
AP
Austin P.
Jun 18, 2024
Fix Spark Python Function
NA
Nasser A.
May 1, 2024
Data Management Fundamentals, Database Design and Implementation
NB
Nisha B.
Jan 30, 2024
SnapLogic: Need a Subject Matter Expert for reviewing MCQ Questions
AC
Ace C.
Nov 7, 2023
Guidance of a hadoop and hive sql statement
Very responsive, nice, and professional!
About Maad
Data Engineer | GCP | BigQuery | Python | SQL | Databricks
Karachi, Pakistan - 7:12 am local time
I have 6+ years of data engineering experience across GCP, AWS, and Azure, working with retail, banking, healthcare, and life sciences data.
RESULTS I'VE DELIVERED
• Reduced BigQuery processing costs by 35% on a 40 TB dataset through partitioning, clustering, and query redesign
• Reduced production incidents by 30% by modernizing Apache Airflow orchestration and cloud runtime
• Saved 25+ engineering hours per week by automating manual data engineering workflows
• Led a cross-cloud migration from Microsoft Fabric/Azure to GCP BigQuery for enterprise analytics
• Unified CRM, ERP, marketing, clickstream, API, and vendor data into analytics-ready datasets
• Built cloud data platforms with defined ingestion, processing, orchestration, and serving layers for batch and near real-time analytics
GCP & BIGQUERY
In my professional data engineering work, I've built and supported production data solutions using Google Cloud, including BigQuery, Cloud Storage, Cloud Functions, Cloud Composer/Airflow, Pub/Sub, and Python.
My work includes building ETL/ELT pipelines, loading and transforming data in BigQuery, automating cloud workflows, optimizing SQL and warehouse performance, and designing reliable data flows for analytics.
My Upwork experience has primarily focused on Python data engineering, SQL, ETL, data processing, PySpark, and Azure Databricks. I bring that same engineering foundation to GCP and BigQuery projects.
WHAT I CAN HELP YOU WITH
BigQuery & GCP
• BigQuery data warehouse development
• BigQuery query and cost optimization
• GCP data pipeline development
• Python → BigQuery ETL pipelines
• API → BigQuery data ingestion
• Cloud Storage → BigQuery pipelines
• Airflow / Cloud Composer orchestration
• Data migration to BigQuery
• GCP data integration and automation
Data Engineering
• Python ETL/ELT pipeline development
• SQL data transformation and processing
• Data integration from APIs, databases, files, CRMs, and other sources
• Data warehouse development
• Data modeling for analytics
• Data quality and validation
• Pipeline monitoring and reliability
• Batch and near real-time data processing
Databricks & Big Data
• Azure Databricks
• PySpark
• Apache Spark
• Delta Lake
• Kafka
• Event Hub
• Lakehouse architecture
LAKEHOUSE & MULTI-CLOUD EXPERIENCE
GCP: BigQuery, Cloud Storage, Cloud Functions, Cloud Composer, Airflow, Pub/Sub, Dataproc, Cloud Scheduler
AWS: S3, Glue, Redshift, Athena, Lambda, EMR
Azure: Databricks, Data Factory, Synapse, ADLS Gen2, Delta Lake, Microsoft Fabric
I've also worked with Apache Iceberg for AWS/S3 lakehouse architectures, including schema evolution, time travel, and scalable querying.
ANALYTICS ENGINEERING
I build analytics-ready datasets using SQL and dbt, with a focus on reliable transformations, data modeling, data quality, and BI-ready data structures.
AI-POWERED DATA AUTOMATION
I also build Python-based data workflows using OpenAI, Anthropic, and Gemini APIs for data extraction, classification, enrichment, and automation.
The focus is production-ready automation with validation, error handling, monitoring, and cost control rather than just proof-of-concept demos.
TECH STACK
Data Engineering: Python, SQL, ETL, ELT, Data Pipelines, Data Warehousing, Data Modeling, Data Integration
GCP: BigQuery, Cloud Storage, Cloud Functions, Cloud Composer, Airflow, Pub/Sub, Dataproc, Cloud Scheduler
AWS: S3, Glue, Redshift, Athena, Lambda, EMR
Azure: Databricks, Data Factory, Synapse, ADLS Gen2, Delta Lake, Microsoft Fabric
Big Data: Apache Spark, PySpark, Kafka, Hive, Apache Iceberg
Analytics Engineering: dbt, SQL, Data Modeling
AI: OpenAI API, Anthropic API, Gemini API, LLM Data Automation
HOW I WORK
You get a clear plan before I start coding, regular progress updates throughout the project, and documentation your team can use after delivery.
I focus on building solutions that are reliable, maintainable, and practical for production, not just code that works once.
If you have an existing pipeline, BigQuery environment, data warehouse, or data integration problem, send me a short description of your current setup and what you're trying to achieve. I'll help you identify the best approach.
Steps for completing your project
After purchasing the project, send requirements so Maad can start the project.
Delivery time starts when Maad receives requirements from you.
Maad works on your project following the steps below.
Revisions may occur after the delivery date.
Data Discovery
Understanding Problem and Data discovery
Data analysis
Exploratory data analysis and feature engineering steps