Rahim A.
Principal Data Engineer at Disney Streaming ML Platform | Designing and Delivering Data Solutions | Machine Learning Platform | AWS | Spark | Databricks
Updated 7 months ago
16+
Years Experience
15
Roles
69
Skills
1
Education
About
Data Engineering Leader and Architect with a proven track record in designing and implementing high impact data solutions across Delta Lake, Lakehouse, Data Lakes, Enterprise Data Warehouses, and Advanced Analytics. Experienced in leading complex projects and teams, driving innovation in real-time/near-real-time streaming solutions and Feature Stores to support machine learning and data science use cases. Skills : Apache Airflow | Apache Spark | Spark Streaming | PySpark | Spark SQL | Apache Flink | Apache Hadoop | HDFS | MapReduce | Presto | Impala | Hue | Sqoop | Apache Kafka | Amazon Kinesis | AWS Glue | Alation | Datadog | Amazon EC2 | Amazon EMR | Amazon SQS | AWS Lambda | Terraform | Jenkins | Spinnaker | Databricks | Data Lake | Delta Lake | Delta Tables | Parquet | CI/CD | API | Python | SQL | HBase | Apache Hive | Snowflake | Amazon S3 | Amazon DynamoDB | Oracle Database | MySQL | Microsoft SQL Server | Teradata | SAP HANA | SingleStore | Neo4j | Tableau | MicroStrategy | ThoughtSpot | Normalization | Star Schema | Snowflake Schema | Fact and Dimension Modeling | Data Quality | Data Architecture | Medallion Architecture
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Experience (15 roles)
5 yrs 11 mos
Current4 roles · Aug 2021 - Present
Principal Data Engineer, Machine Learning Engineering
Current• Designed and architected scalable machine learning platforms for batch and real time inference • Developed data infrastructure supporting ML teams for use cases such as search, recommendation, paid sharing, personalization, and subscriber growth. • Designed and built a hybrid feature store levera...
Principal Data Engineer, Engagement and Retention Data Solutions
• I architected, designed, and led the development of Campaign360 DeltaLake (Medallion architecture), centralizing Disney's campaign data to deliver actionable subscriber engagement insights for analytical and operational teams. • Contributed to and maintained Aegis Service, which generates persona...
Lead Data Engineer - Engagement and Retention Data Solutions
• Designed and executed data engineering solutions for data analytics and data science use cases, employing a cloud-based data tech stack including Airflow, Databricks, Snowflake, AWS, Enterprise GitHub, Spark, Python, Terraform, S3, Glue, SQS, EC2. • AWS resource deployment and management using Te...
Lead Software Engineer - Data and Analytics
Led Quality Engineering efforts for below projects. • Sessions360 Data Mart • ESPN+ PPV
2 roles · Oct 2019 - Aug 2021
Senior Software Developer - Data and Analytics
• Quality Engineering for projects - Sessions360 Data Mart and ThoughtSpot Cloud migration. • Implemented a cloud data quality framework (Spark, EMR) to uphold data integrity across diverse platforms, encompassing Hive/HDFS, Snowflake, SingleStore (distributed RDBMS), ThoughtSpot, and S3.
Sr Data Engineer Test - Data and Analytics
• Quality Engineering for projects such as Ads re-architecture (involving HDFS/Hive-Snowflake migration), migration to HDFS secure clusters, and implementation of California Consumer Privacy Act (CCPA).
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Education (1)
SUNY New Paltz
Skills (69)
Certifications (11)▼
Databricks Certified Data Engineer Professional
Databricks
Python for Data Science and AI
Coursera
Quick Reference: Spark Architecture
Databricks
Snowflake Data Engineer Training
Snowflake
Fundamentals of Delta Lake Accreditation
Databricks
Leveraging Cloud Computing for IoT
Introduction to Unified Data Analytics with Databricks
Databricks
Modernizing Data Lakes and Data Warehouses with GCP
Coursera
Fundamentals of Delta Lake
Databricks
Distributed Computing with Spark SQL
Coursera
Fundamentals of SQL on Databricks
Databricks
Honors & Awards (1)▼
1st Place - Disney Datathon
Issued by Disney Streaming Services · Aug 2022
Associated with Disney Streaming Our team secured the first position in the Highest Value category with a project that employed machine learning to classify JIRA tickets. Using historical text data, we trained bots to categorize tickets. Central to this initiative is a precisely engineered text classification model, leveraging advanced data engineering techniques.This innovation assigns work types based on epic names, effectively enhancing ticket management efficiency.
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