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I am a data engineer with 3+ years of experience designing and scaling cloud-native data pipelines across Azure and AWS. I specialize in ETL/ELT, batch and streaming data processing, and analytics-ready datasets using Databricks, Spark, PySpark, SQL, Airflow, dbt, Kafka, and Terraform. I have a proven track record of reducing data latency by 45% and maintaining 99.8% SLA compliance, enabling faster BI and ML workflows. I enjoy turning complex data into reliable, scalable infrastructure, collaborating with cross-functional teams, and delivering governance, lineage, and automated deployment pipelines to support data-driven decision making.…I am a data engineer with 3+ years of experience designing and scaling cloud-native data pipelines across Azure and AWS. I specialize in ETL/ELT, batch and streaming data processing, and analytics-ready datasets using Databricks, Spark, PySpark, SQL, Airflow, dbt, Kafka, and Terraform. I have a proven track record of reducing data latency by 45% and maintaining 99.8% SLA compliance, enabling faster BI and ML workflows. I enjoy turning complex data into reliable, scalable infrastructure, collaborating with cross-functional teams, and delivering governance, lineage, and automated deployment pipelines to support data-driven decision making.

Mohammed Abdul Rahman Kalwathi

Data Scientist, Data Analyst, Web Developer, +2

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I am a data engineer with 3+ years of experience designing and scaling cloud-native data pipelines across Azure and AWS. I specialize in ETL/ELT, batch and streaming data processing, and analytics-ready datasets using Databricks, Spark, PySpark, SQL, Airflow, dbt, Kafka, and Terraform. I have a proven track record of reducing data latency by 45% and maintaining 99.8% SLA compliance, enabling faster BI and ML workflows. I enjoy turning complex data into reliable, scalable infrastructure, collaborating with cross-functional teams, and delivering governance, lineage, and automated deployment pipelines to support data-driven decision making.…I am a data engineer with 3+ years of experience designing and scaling cloud-native data pipelines across Azure and AWS. I specialize in ETL/ELT, batch and streaming data processing, and analytics-ready datasets using Databricks, Spark, PySpark, SQL, Airflow, dbt, Kafka, and Terraform. I have a proven track record of reducing data latency by 45% and maintaining 99.8% SLA compliance, enabling faster BI and ML workflows. I enjoy turning complex data into reliable, scalable infrastructure, collaborating with cross-functional teams, and delivering governance, lineage, and automated deployment pipelines to support data-driven decision making.

Available to hire

I am a data engineer with 3+ years of experience designing and scaling cloud-native data pipelines across Azure and AWS. I specialize in ETL/ELT, batch and streaming data processing, and analytics-ready datasets using Databricks, Spark, PySpark, SQL, Airflow, dbt, Kafka, and Terraform. I have a proven track record of reducing data latency by 45% and maintaining 99.8% SLA compliance, enabling faster BI and ML workflows.

I enjoy turning complex data into reliable, scalable infrastructure, collaborating with cross-functional teams, and delivering governance, lineage, and automated deployment pipelines to support data-driven decision making.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Data Engineer at Community Dreams Foundation

February 1, 2025 - Present

Architected and developed enterprise-scale ETL/ELT pipelines on Databricks (Spark, PySpark, Spark SQL) processing 2+ TB/day; reduced end-to-end latency by 45% through partitioning and tuning. Orchestrated batch and streaming workloads using Azure Data Factory, Apache Airflow, and dbt to deliver analytics-ready datasets with 99.8% SLA compliance. Implemented dimensional data models (Star and Snowflake schemas) in Snowflake and Azure Synapse to support self-service BI and downstream ML workflows. Enabled real-time data ingestion using Kafka and Delta Lake on Databricks, achieving sub-minute data availability. Standardized CI/CD and infrastructure automation with GitHub Actions and Terraform, improving deployment reliability by 60%.

Data Engineer at Axtria - Ingenious Insights

April 1, 2023 - August 1, 2023

Delivered scalable AWS-based data pipelines using AWS Glue, Spark, S3, and Redshift, processing 5+ TB/month while maintaining 99.8% SLA adherence. Established workflow orchestration and dependency management across Apache Airflow and Control-M, reducing data refresh cycles by 50% and eliminating manual operational effort. Drove performance optimization across Databricks, Snowflake, and Redshift through query refactoring, partition pruning, and caching, cutting pipeline runtimes by 45%. Produced analytics-ready datasets and BI layers enabling self-service reporting and contributed to a 25% improvement in ML model accuracy via feature pipelines and MLflow. Defined data governance, lineage, and documentation standards across pipelines and warehouses, reducing new engineer onboarding time by 40%.

Analyst Trainee at Axtria - Ingenious Insights

March 1, 2022 - April 1, 2023

Constructed enterprise-grade analytical datasets by integrating CRM and MDM sources, ensuring consistent and trusted KPIs across downstream reporting platforms. Re-modeled large analytical tables using Star and Snowflake schemas, improving query scalability and reducing execution times by 40% through optimized modeling and partitioning. Executed large-scale data migrations by moving 100+ Hadoop tables to Amazon Redshift using Spark-based batch pipelines, achieving 50% performance gains and simplifying analytics access. Instituted automated data quality and validation frameworks in Python and SQL, enforcing business rules to achieve 99.5% data accuracy and reduce QA effort by 80%.

Data Analyst at Capillary Technologies

February 1, 2021 - January 1, 2022

Automated end-to-end ETL pipelines using Python, Airflow, and PostgreSQL, improving pipeline reliability and reducing manual operational effort by 35%. Supported forecasting and optimization workloads by building scalable data pipelines for Scikit-learn and TensorFlow models, generating $250K in annual cost savings through improved execution reliability. Provisioned production-grade analytics datasets across Oracle, PostgreSQL, Tableau, and Excel (VBA), reducing reporting turnaround time by 50% while standardizing KPI delivery.