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Data Systems Scala Developers

Built end-to-end pipelines and APIs for historical and near-real-time news analytics, supporting customer data access and ongoing dataset updates. Improved ingestion efficiency by consolidating database writes through batch processing, reducing per-record overhead.

• Functional program design in Scala
• Big-data analysis with Scala and Spark
• Published research on intelligent interactive systems
• Distributed data-platform engineering

Engineered large-scale data processing pipelines using Apache Spark and Scala, significantly enhancing data throughput. Developed real-time data ingestion systems with Apache Kafka, ensuring high availability and fault tolerance. Expert in streaming analytics, applying Flink for dynamic data transformations in IoT applications.

• Integrated Scala Play for responsive web data visualization
• Utilized Cats Effect for functional programming concurrency control
• Designed a custom schema registry for Kafka message validation
• Implemented end-to-end data encryption in distributed environments

Led the design and implementation of distributed data processing systems handling petabyte-scale datasets. Spearheaded the integration of Kafka with Spark for real-time analytics pipelines in financial services. Architected a high-performance data ingestion framework using Akka to optimize processing speed and reliability.

• Developed custom SQL query optimization techniques for large datasets
• Implemented Git versioning for complex data workflows
• Pioneered Akka-based microservices for scalable data processing
• Optimized Spark jobs reducing runtime by 30%

Developed large-scale distributed data processing systems for real-time analytics. Specialized in building high-performance ETL pipelines and data warehousing solutions. Designed and optimized custom consensus protocols for fault-tolerant systems.

• Implemented complex event-driven architectures for financial services.
• Architected scalable microservices using Go and Rust.
• Enhanced database internals for improved query performance.

Engineered large-scale data processing frameworks leveraging Scala and Apache Spark for real-time analytics. Integrated complex event processing on cloud platforms, achieving seamless interoperability across AWS and Google Cloud. Pioneered the deployment of robust streaming solutions with Kafka, ensuring high-throughput data ingestion.

• Implemented Airflow for orchestrating complex ETL workflows.
• Designed scalable data lakes on Google Cloud Storage.
• Enhanced Spark job performance with custom memory management.
• Built self-healing data pipelines with automated fault recovery.

Architected large-scale data pipelines using Apache Spark and Scala, optimizing for real-time analytics in the e-commerce domain. Spearheaded the migration of data infrastructure to Databricks, enhancing processing speed and reliability. Developed a custom data validation framework in Python, ensuring data integrity across distributed systems.

• Implemented Rust-based data transformation tools for performance gains
• Designed SQL-based ETL workflows for complex data models
• Automated data quality checks using custom-built Python scripts

Designed MLS group-state features for secure messaging, including per-field permissions, in-place migration of live groups, and invite flows based on external commits. Built a Rust and Go bidirectional streaming transport with ordered delivery, heartbeats, reconnect handling, and database transaction boundaries enforced through async traits.

• Reduced 100-device invite payloads from 8 MB to 200 KB
• Cut full group loads from 13 database reads to one
• Built Rust-backed GraphQL and FFI layers for untrusted drivers
• Worked on edge computer-vision and blockchain-indexing systems

Built a medallion-based data platform with Airflow and PySpark to standardize raw-material, production, supplier, and pricing data for margin analysis. Developed cross-entity data integration that mapped product relationships across heterogeneous source systems, enabling end-to-end product and cost traceability.

• Deployed data workloads across Azure and Kubernetes
• Modeled analytical data in PostgreSQL
• Instrumented pipelines and services with Grafana
• Refactored legacy Scala and TypeScript with expanded unit coverage

Replaced a costly external service with an in-house near-real-time platform processing 1.5 billion events daily, reducing latency from 15 minutes to 5. Designed custom watermark joins and dependency handling for late-arriving data, preserving unmatched records until upstream datasets were complete and correctly matched.

• Built Spark and Airflow production pipelines
• Operated workloads on Kubernetes and AWS
• Delivered audience data to roughly 30 partners
• Used Aerospike for durable intermediate state

Built full-stack systems spanning frontend delivery, backend services, relational database design, and deployment operations. Implemented Kafka-based ingestion across multiple servers with Spark processing for real-time analytics and data storage.

• Erlang development on the BEAM runtime
• Continuous integration for rapidly changing feature releases
• ScalaTest, Cucumber, and Selenium test automation
• Unit, acceptance, integration, and performance testing

Specialized in building data pipelines using Scala and Java for large-scale ETL processes. Designed graph database solutions using Cypher and CQL to improve data retrieval efficiency.

• Automated data processing workflows with Groovy
• Enhanced data integration using Python scripts
• Implemented graph algorithms for advanced data insights

Developed machine learning pipelines for real-time data analysis and prediction. Deployed distributed systems on AWS Fargate for scalable processing.

• Optimized text processing with TF-IDF and k-NN
• Automated data migration using AWS DMS
• Implemented dimensionality reduction with PCA for feature optimization

Developed high-performance distributed systems for financial trading platforms, optimizing latency and throughput for real-time data processing. Designed and implemented scalable consensus protocols for blockchain networks, enhancing security and reliability. Spearheaded the migration of legacy systems to cloud-native architectures, significantly improving deployment efficiency and scalability.

• Architected microservices for telecom billing systems handling millions of transactions
• Built custom compilers for domain-specific languages in the automotive industry
• Implemented machine learning pipelines for predictive analytics in healthcare
• Optimized graph-based algorithms for social network analysis applications

Designed and implemented robust data pipelines using PySpark and Scala for large-scale analytics. Developed and optimized Lakehouse and Medallion architectures to streamline data processing and storage. Spearheaded the migration of legacy data systems to cloud-based solutions, improving performance and scalability.

• Built real-time data ingestion frameworks with Python and SQL
• Engineered automated ETL processes for high-volume data environments
• Enhanced data governance through advanced schema management techniques