Tallinn, Harjumaa, Estonia
Experienced IT professional with over 7+ years of expertise in the Azure cloud ecosystem, development, maintenance, and big data analytics, including hands-on work with BI and ETL tools. Known for strong technical skills, problem-solving ability, and a passion for learning emerging technologies. Adept at collaborating with business users, consultants, and project managers to deliver effective solutions in global team environments.
Architected and implemented scalable ETL pipelines for integrating multi-carrier logistics data into a unified data lake using Azure Data Factory and Databricks. Enabled real-time delivery tracking and last-mile analytics by ingesting and transforming high-volume shipping data from retail and logistics partners. Built layered architecture (Raw → Staging → Curated) in ADLS to support fast querying and operational dashboards in Power BI. Developed transformation logic in PySpark to standardize delivery event streams, handle SLA breaches, and flag exceptions. Led the data platform's modernization initiative, migrating on-prem ETL workloads to Azure cloud infrastructure. Collaborated with supply chain teams to deliver custom analytics for carrier performance, delivery delays, and cost per shipment. Implemented robust monitoring and alerting for ETL pipelines using logging and error-handling features. Provided architecture guidance and mentoring to junior developers to ensure consistency in solution delivery and performance optimization.
Designed and developed scalable ETL processes to collect, clean, and enrich web visitor intelligence data from global websites and third-party tools. Built streaming and batch pipelines using ADF, Databricks, and Azure Functions, supporting marketing attribution models and lead scoring. Integrated behavioral and CRM data into a centralized Azure Data Lake for reporting and predictive analytics. Delivered analytics-ready datasets for Power BI, enabling the business to monitor funnel conversion and campaign effectiveness. Worked with data scientists to build data pipelines that fed segmentation and intent prediction models for lead enrichment. Led improvements in pipeline performance through optimization of Spark jobs and cost-effective Azure configurations. Developed data quality frameworks to flag anomalies and implemented validation rules for schema enforcement. Partnered with cross-functional product and engineering teams to define data contracts and ensure data governance compliance.
Built robust ETL workflows to collect and transform large-scale serverless observability data (AWS Lambda logs, metrics, traces) using Spark and Python. Collected real-time telemetry streams into Azure Data Lake and Cosmos DB, supporting error tracking and system health dashboards. Developed transformation logic to normalize JSON logs and structure them for reporting and alerting use cases. Supported back-end data needs of product teams by preparing API-ready datasets from raw infrastructure data. Migrated log aggregation pipelines to a more scalable Delta Lake architecture, improving query response times. Enabled observability analytics dashboards in Power BI to surface issues like cold starts, high latency, or invocation errors. Worked closely with DevOps to ensure efficient deployment and monitoring of data workflows in production environments. Conducted thorough documentation and testing of data flows to support compliance and system reliability.