Sofia, Sofia City, Bulgaria
I am a Data Platform and Software Engineering professional with 12+ years of experience building scalable data platforms, analytics solutions, and cloud-native applications. My expertise spans data engineering, streaming architectures, distributed systems, and real-time analytics, with a strong focus on delivering reliable, high-quality data products that drive business decisions. Currently, I serve as a Tech Lead within the Data Platform team at the Financial Times, where I lead the development of data ingestion, processing, and analytics solutions supporting Business Intelligence, Data Science, Marketing, and Analytics teams. I am responsible for building and operating scalable platforms that collect, process, and deliver data from a wide range of internal and external sources. A key part of my work is enabling the Financial Times' next-generation clickstream analytics platform, helping transform audience engagement data into actionable insights that power analytics, reporting, customer understanding, and product decisions. Prior to the Financial Times, I worked on large-scale data processing, search, and distributed systems, developing solutions using Hadoop, Kafka, Cassandra, Storm, Solr, and AWS. I enjoy solving complex engineering challenges, designing scalable architectures, improving platform reliability, and mentoring engineers while driving technical excellence. Core expertise: • Data Platform Engineering • Streaming & Event-Driven Architectures • Data Ingestion & Processing Pipelines • Analytics & Clickstream Platforms • ETL/ELT & Data Lake Architectures • Cloud-Native Systems & Microservices • Technical Leadership & Team Mentoring Technologies: Java, Dropwizard, Spring Boot, Python, Apache Kafka, Apache Flink, Apache Airflow, AWS (Lambda, Kinesis, S3, Redshift, Athena, Kinesis Firehose, Glue, S3 Tables, Schema Registry, DynamoDB, ECS, EKS, IAM), BigQuery, Apache Iceberg, Docker, Kubernetes, Cassandra, Hadoop, and Solr.
• Tech Lead within the Financial Times Data Platform team, responsible for designing, building, and operating scalable data platforms that enable Analytics, Business Intelligence, Data Science, and Marketing use cases. • Lead the development and maintenance of data ingestion and processing pipelines that collect data from a wide range of internal and external sources, including APIs, Amazon S3, Kafka/MSK, Kinesis, and flat-file integrations. • Own key components of Spoor, the Financial Times' next-generation clickstream analytics platform, processing and delivering user engagement and audience interaction data to downstream consumers across the organization. • Design and implement batch and real-time data processing solutions that transform raw operational and behavioral data into trusted, business-ready datasets. • Architect and develop event-driven and streaming data solutions using Apache Kafka/MSK, Apache Flink, AWS Kinesis, SNS, and SQS to support scalable and reliable data processing. • Build and maintain cloud-native data services and microservices using Java, Python, Docker, Kubernetes, and AWS technologies including ECS, EKS, Lambda, ECR, S3, Athena, IAM, KMS, Route 53, and Redshift. • Drive the adoption of modern data lake and analytical technologies, including Apache Iceberg, Redshift Spectrum, and BigQuery, improving scalability, performance, and accessibility of enterprise data. • Lead a team of engineers responsible for delivering high-quality data products, platform services, and analytical capabilities while promoting engineering best practices, operational excellence, and continuous improvement. • Partner with stakeholders across engineering, analytics, marketing, and data science teams to ensure the availability, reliability, and quality of critical business data assets. • Contribute to technical strategy, architecture decisions, platform modernization initiatives, and the long-term evolution of the Financial Times data ecosystem.
Client: Financial Times • Tech Lead within the Financial Times Data Platform team, responsible for designing, building, and operating scalable data platforms that enable Analytics, Business Intelligence, Data Science, and Marketing use cases. • Lead the development and maintenance of data ingestion and processing pipelines that collect data from a wide range of internal and external sources, including APIs, Amazon S3, Kafka/MSK, Kinesis, and flat-file integrations. • Own key components of Spoor, the Financial Times' next-generation clickstream analytics platform, processing and delivering user engagement and audience interaction data to downstream consumers across the organization. • Design and implement batch and real-time data processing solutions that transform raw operational and behavioral data into trusted, business-ready datasets. • Architect and develop event-driven and streaming data solutions using Apache Kafka/MSK, Apache Flink, AWS Kinesis, SNS, and SQS to support scalable and reliable data processing. • Build and maintain cloud-native data services and microservices using Java, Python, Docker, Kubernetes, and AWS technologies including ECS, EKS, Lambda, ECR, S3, Athena, IAM, KMS, Route 53, and Redshift. • Drive the adoption of modern data lake and analytical technologies, including Apache Iceberg, Redshift Spectrum, and BigQuery, improving scalability, performance, and accessibility of enterprise data. • Lead a team of engineers responsible for delivering high-quality data products, platform services, and analytical capabilities while promoting engineering best practices, operational excellence, and continuous improvement. • Partner with stakeholders across engineering, analytics, marketing, and data science teams to ensure the availability, reliability, and quality of critical business data assets.
• Designed and developed multiple platform components across successive product releases, collaborating with stakeholders to analyze requirements and deliver scalable solutions. • Built and maintained real-time data processing pipelines using Apache Kafka, Apache Storm, and Cassandra to process and persist high-volume event streams. • Developed large-scale data ingestion and transformation workflows using Hadoop HDFS and MapReduce, enabling efficient processing of enterprise datasets. • Automated data movement and workflow orchestration using Apache Flume and Oozie, improving reliability and operational efficiency of data pipelines. • Implemented full-text search capabilities using Apache Solr, enabling fast and accurate retrieval of indexed content. • Managed metadata and reporting for crawled files across multiple file systems using Cassandra, ensuring scalable and reliable data storage. • Developed responsive user interfaces using jQuery, Backbone.js, and Handlebars.js to enhance user experience and application usability. • Built document viewing and content delivery services that enabled users to access crawled files in standardized formats. • Developed file production and redaction services to generate native and alternative document formats while supporting compliance and legal workflows. • Deployed and maintained distributed services on Amazon Web Services (AWS), contributing to cloud adoption and operational scalability.