Reetesh Reddy
Data Science Engineer @BlackRock
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WORK HISTORY
Data Science Engineer @BlackRock
VA, US
Built a low-latency, real-time market data pipeline using Apache Kafka, AWS Glue, and Spark Structured Streaming to process live tick data from exchanges, reducing end-to-end data latency by 40%.• Enabled faster analytics by migrating over 1 TB of historical financial tick data from on-prem Hadoop to Snowflake via AWS S3, improving query performance by 20% and lowering infrastructure costs.• Automated regulatory data pipelines by deploying 5+ Apache Airflow DAGs, enhancing ETL reliability for MiFID II and SEC reports and reducing job failures by 20%.• Optimized data compliance and traceability by integrating Delta Lake with ACID guarantees and time travel on AWS S3, supporting 24-month rollback for institutional trade data audits.• Developed a modular data modeling layer by converting 50+ SQL transformations into reusable dbt models, boosting team productivity and reducing code duplication.• Improved data lineage and governance by version-controlling transformations and documenting models using dbt, enabling better collaboration across data engineering and analytics teams.• Streamlined ETL orchestration by designing fault-tolerant DAGs with built-in retries, SLA alerts, and task-level monitoring for regulatory-critical data flows.• Enhanced data lake performance by applying Delta Lake’s schema enforcement and compaction strategies, resulting in faster downstream processing for analytical workloads.• Reduced data transfer overhead and storage cost through optimized partitioning and compression strategies during Hadoop-to-Snowflake migration.• Contributed to enterprise-grade data infrastructure supporting BlackRock’s Aladdin platform, enabling high-availability data ingestion and analytics across investment decision pipelines.
EDUCATION
University of North Texas
Masters Degree, Advance Data Analytics
ABOUT REETESH REDDY
4+ years of Progressively responsible experience as Data Engineer in Data Engineering, Data Analysis, ETL, and Data warehousing, Business Intelligence background in Designing, Developing, Analysis, Implementation, and post implementation support of DWBI applications with a proven track record of designing and implementing robust and scalable data solutions.• Expertise in ETL processes, data modeling, and database management, ensuring optimal data flow, organization, and retrieval.• Proficient in utilizing cloud platforms, including AWS and Azure, to architect and implement end-to-end data pipelines for diverse business needs.• Skilled in leveraging big data technologies such as Apache Spark and Hadoop to process and analyze large datasets efficiently.• Experienced in implementing data quality and governance measures, ensuring data accuracy, compliance, and security.• Demonstrated ability to collaborate with cross-functional teams to understand business requirements and translate them into effective data solutions.• Strong background in optimizing database performance, conducting query tuning, and implementing best practices for efficient data storage and retrieval.• Expertise in designing and implementing reusable ETL/ELT frameworks using Python, Scala, Apache Spark, Spark SQL, Java, Kafka Connect, AWS, Azure, and other Big Data technologies in Cloud Platform.• Good working knowledge of Amazon Web Services (AWS) Cloud Platform which includes services like EC2, S3, VPC, ELB, IAM, DynamoDB, Cloud Front, Route 53, Elastic Beanstalk (EBS), Security Groups, Auto Scaling, Redshift, CloudWatch, CloudFormation, CloudTrail, Ops Works, Kinesis, SQS, SNS, SES.• Demonstrated expertise in relational databases, with a proven track record of creating complex queries and performing optimizations using SQL.• Experience developing Kafka producers and Kafka Consumers for streaming millions of events on streaming data.• Experience on Migrating SQL database to Azure data Lake, Azure data lake Analytics, Azure SQL Database, Data Bricks and Azure SQL Data warehouse.• Solid experience in Hadoop distributed file system (HDFS), Sqoop, Hive, HBase, Spark, MapReduce, Ambari, Kafka, Yarn, Airflow, Flume, Oozie, Zookeeper and pig.• Hands-on experience with AWS platform along with Terraform and DevOps Suite including Git and Jenkins. AWS Certified Solutions Architect - Professional.• Engineered data pipelines in Azure Data Factory (ADF), orchestrating ETL processes from diverse sources to Azure SQL, Blob storage, and Azure SQL Data Warehouse.
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