Data Engineer & Cloud Data Architect | AWS, GCP, Data Pipelines

Overview

Looking for part-time work (4 hours/day)

at $16.59/hour ($1,600.00/month)

Bachelors degree

Last Active

June 3rd, 2026 (7 days ago)

Member Since

August 1st, 2020

Profile Description

I am a Data Engineering Team Lead with 5+ years of experience designing, building, and scaling enterprise-grade data platforms and cloud-native data pipelines. I specialize in delivering end-to-end data solutions that improve data reliability, operational efficiency, and business decision-making across complex environments.

I currently lead a cross-functional data engineering team, driving architecture decisions, engineering best practices, and hands-on development of scalable data systems across AWS and hybrid cloud ecosystems. My work spans large-scale data migrations, pipeline modernization, and building observability platforms that improve data reliability and system transparency.

I have strong experience working across diverse industries including publishing, telecommunications, fintech, adtech, and real estate, giving me a broad understanding of business data challenges and how to solve them efficiently.

SERVICES I OFFER:
- End-to-End Data Pipeline Development: Design and build scalable, production-ready ETL/ELT pipelines using modern cloud and big data tools.

- Data Architecture Design & Modernization: Create and optimize data lake, data warehouse, and hybrid architectures for performance, scalability, and cost efficiency.

- Cloud Data Engineering Solutions: Build and deploy data systems using AWS, GCP, and Azure services including serverless and distributed computing frameworks.

- Data Migration & Integration: Migrate legacy/on-prem systems to cloud platforms and integrate multiple data sources (APIs, databases, SaaS, and files).

- Data Automation & Orchestration: Automate workflows using tools like Airflow, AWS Step Functions, NiFi, and dbt to reduce manual effort and improve reliability.

- Data Quality & Observability: Implement monitoring, logging, and alerting systems to ensure data accuracy, pipeline health, and operational visibility.

- Business Intelligence Enablement: Build dashboards and data models using Power BI, Tableau, and Looker Studio for actionable insights.

TECHNICAL STACK
- Programming Languages: Python (Pandas, PySpark), Scala (Spark), SQL, JavaScript
- Cloud Platforms: AWS, Google Cloud Platform (GCP), Microsoft Azure, Cloudera Data Platform
- Big Data & Processing: Apache Spark, Hadoop, Hive, Kafka, Apache Iceberg
- Data Engineering Tools: Airflow, dbt, AWS Glue, Fivetran, HVR, Apache NiFi, SSIS, Dataflow, Data Fusion
- Databases & Warehouses: BigQuery, Redshift, PostgreSQL, MySQL, SQL Server, Aurora RDS- - Storage & Infrastructure: Amazon S3, HDFS, Azure Blob Storage, AWS EC2, VPC, Lambda, Cloud Functions
- BI & Visualization: Power BI, Tableau, Looker Studio
- CI/CD & Version Control: GitHub, GitLab