You will get a modern Data Lakehouse architecture built on Databricks, AWS, or GCP
Project details
I will help you design and build a production grade modern Data Lakehouse architecture on Databricks, AWS, or GCP with Bronze, Silver, and Gold layers scalable, governed, and analytics ready.
With 10+ years of Data Engineering experience, I have built lakehouse platforms for clients across finance, retail, telecom, and tech using Apache Iceberg, Delta Lake, Apache Hudi, and Databricks.
What is included:
• Lakehouse architecture design with Bronze, Silver, and Gold layers
• Apache Iceberg or Delta Lake table format implementation
• Apache Spark based data processing and transformation
• dbt transformation layer on top of lakehouse
• Data catalog and governance setup
Perfect for you if:
• You are building a new data platform from scratch
• You want to replace your data warehouse with a flexible lakehouse
• Your current setup cannot handle growing data volumes
• You need both flexibility of a data lake and reliability of a warehouse
Every delivery includes full architecture documentation, Spark pipelines, dbt models, and governance setup so your team can maintain and scale it confidently.
Message me before ordering so I can recommend the right package.
With 10+ years of Data Engineering experience, I have built lakehouse platforms for clients across finance, retail, telecom, and tech using Apache Iceberg, Delta Lake, Apache Hudi, and Databricks.
What is included:
• Lakehouse architecture design with Bronze, Silver, and Gold layers
• Apache Iceberg or Delta Lake table format implementation
• Apache Spark based data processing and transformation
• dbt transformation layer on top of lakehouse
• Data catalog and governance setup
Perfect for you if:
• You are building a new data platform from scratch
• You want to replace your data warehouse with a flexible lakehouse
• Your current setup cannot handle growing data volumes
• You need both flexibility of a data lake and reliability of a warehouse
Every delivery includes full architecture documentation, Spark pipelines, dbt models, and governance setup so your team can maintain and scale it confidently.
Message me before ordering so I can recommend the right package.
Database Type
PostgreSQL, MongoDB, TeradataWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$1,600
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Source Code |
About Nofel
Cloud Data Engineer | AWS, Azure, DevOps | Snowflake, dbt, Databricks
Lahore, Pakistan - 6:01 pm local time
I build production grade data solutions for clients across finance, retail, telecom, and tech supporting both startups and large enterprises. My work spans greenfield data platform builds, legacy ETL modernization, cloud migrations, and real time streaming architectures ensuring every pipeline is reliable, observable, and built to scale.
I specialize in end to end Data Engineering delivery:
- ETL/ELT pipeline development, orchestration, and automation (Airflow, AWS Glue, Azure Data Factory, Fivetran, Apache Beam)
- Real time and streaming architectures (Apache Kafka, Apache Flink, AWS Kinesis, Google Pub/Sub, Debezium, CDC)
- Data transformation and modeling using dbt (data build tool) with fully tested and documented dbt models
- Cloud Data Warehouse design and optimization (Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse Analytics)
- Data Lakehouse architecture with open table formats (Apache Iceberg, Delta Lake, Apache Hudi) on Databricks and cloud storage
- Big data processing and distributed computing (Apache Spark, Spark SQL, Trino, Presto, Athena)
- Data Quality frameworks and observability (Great Expectations, Soda)
Data Governance, Data Lineage, Data Catalog, and Metadata Management (OpenMetadata, DataHub)
- Multi cloud data platform design across AWS (S3, Glue, Redshift, Athena), GCP (BigQuery, Pub/Sub, Cloud Storage), and Azure (Synapse, Data Lake, Data Factory)
Pipeline stabilization, performance optimization, and cost reduction for underperforming data systems
How I work:
I follow proven data engineering principles to ensure:
- Pipelines align with business goals and analytical requirements
- Technical debt, bottlenecks, and data quality risks are eliminated early
- Architectures remain modular, maintainable, and cloud agnostic where possible
- Platforms scale reliably with data volume, team size, and workload growth
I treat data infrastructure as a business enabler, not just a technical implementation. Every design decision focuses on reducing pipeline failures, improving data reliability, and accelerating time to insight for your teams.
The difference I bring:
You can use modern data engineering to:
- Build scalable ETL/ELT pipelines that handle growing data volumes without breaking
- Move from batch only to real time streaming with Kafka, Flink, and Kinesis
- Migrate legacy data systems to modern cloud platforms like Snowflake or Databricks
- Implement a Data Lakehouse that gives you both flexibility and warehouse grade reliability
- Enforce Data Governance, Data Lineage, and Data Quality across your entire data platform
- Replace fragmented, manual, or unreliable data workflows with automated, observable pipelines
Let's build a data platform that actually scales.
Steps for completing your project
After purchasing the project, send requirements so Nofel can start the project.
Delivery time starts when Nofel receives requirements from you.
Nofel works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Gathering
Review data sources, cloud platform, volume, and analytics goals to finalize lakehouse architecture scope.
Architecture Design
Design end to end lakehouse architecture with Bronze, Silver, and Gold layer definitions and data flow diagrams.