Farman Ali

Designed and operated a GCP-based enterprise data platform focused on scalable analytics engineering and governed data delivery. Built and maintained batch and near real-time ELT/ETL pipelines using BigQuery, Cloud Composer, dbt, Dataflow, Dataproc, Pub/Sub, Airbyte Cloud, and Cloud Storage.

Built end-to-end GCP data pipelines using BigQuery, Cloud Composer, dbt, Dataflow, Dataproc, and Airbyte Cloud for enterprise analytics use cases. Developed reusable dbt transformation frameworks with incremental models, testing, freshness checks, and documentation.

Designed and operated production-grade cloud data pipelines on AWS, supporting scalable ingestion, transformation, analytics, and downstream data consumption. Built end-to-end workflows using S3, AWS Glue, EMR, Lambda, Step Functions, Redshift, and Athena.

Built and operated enterprise-scale, Snowflake-centric data platforms in a regulated financial environment, leveraging data mesh architecture. Designed and optimized data pipelines using Snowflake, Azure Databricks, ADF, NiFi, and dbt.

Designed, implemented, and optimized ELT/ETL pipelines using Matillion, dbt, Snowflake, and AWS Glue, delivering high-quality, ML-ready, and analytics-ready datasets. Built feature-oriented and dimensional data models with dbt, enabling self-service analytics and ML experimentation.

Designed and implemented large-scale batch and streaming data platforms on GCP for analytics and enterprise data processing. Built data pipelines using Pub/Sub, Dataproc, Dataflow, and Spark, supporting scalable ingestion and transformation workflows.

Designed and implemented enterprise data warehouse and big data platforms using Cloudera, Hadoop, Spark, Hive, and NiFi, supporting analytics, reporting, and ML use cases. Built large-scale ingestion pipelines handling structured, semi-structured, and unstructured data.

Pursued a BS in Software Engineering, gaining a strong foundation in software development and engineering principles.