Zain A.

Data Engineer | Databricks | Snowflake | Azure | AWS | GCP | Spark | ETL/ELT I help companies build, optimize, and maintain reliable data pipelines and cloud data platforms that turn raw data into analytics-ready information. With 8+ years of Data Engineering experience, I have worked across financial services, telecom, trading, sourcing, and eCommerce environments, handling everything from large-scale data ingestion and ETL development to data warehousing, pipeline optimization, and cloud migration. What I can help you with Data Engineering & ETL/ELT – Build robust and scalable data pipelines using Python, SQL, Spark/PySpark, Airflow, Azure Data Factory, SSIS, Informatica, Prefect and other orchestration tools. Databricks & Spark – Develop and optimize Spark workloads, data transformations and large-scale processing pipelines. Snowflake – Data warehouse development, data transformation, optimization and integration. Cloud Data Engineering – Hands-on experience across Azure, AWS and GCP, as well as cloud-agnostic platforms such as Snowflake and Databricks. Data Warehousing & Data Lakes – Design and develop scalable solutions using technologies such as Snowflake, BigQuery, S3, SQL Server, Teradata, Vertica and Hadoop. Pipeline Optimization – Troubleshoot slow or unreliable data pipelines, improve processing performance and optimize data workflows. Data Quality & Reliability – Build processes that improve data accuracy, consistency, validation and pipeline reliability. CI/CD & Production Data Workflows – Experience working with Agile development methodologies and CI/CD environments. Why clients work with me I don't just focus on writing ETL code. I focus on understanding where the data comes from, how it needs to be transformed, how it will be consumed, and how the solution can remain reliable and scalable in production. Whether you need a new data pipeline, help fixing an existing ETL process, a Databricks/Spark optimization, Snowflake development, or assistance with a cloud data platform, I can help take the project from requirement → implementation → production. If you have a data pipeline that needs to be built, fixed, optimized, or migrated, let's discuss it.