Davide Imperati

• Federated data across three major brands and a few minor ones
• Defined a target data model and guided the migration from legacy systems to the Snowflake data warehouse
• Implemented master data management in the SaaS vendor tool (Step by Stibo)
• Extracted data from legacy systems, transformed it for compatibility with the new data model, and loaded it into the data warehouse preserving lineage
• Enabled the frontend team to fetch data from the unified system to support cross-selling across brands
• Centralized data governance by enabling data stewards to perform governance according to business functions
• Implemented logging, monitoring, and enhanced aggregate data visualization (basic analytics) under dual-run constraints over two years

Technologies: Python, Pandas, SciPy, FastAPI, Flask, Django, Git, GitHub, Jenkins, Jira, ClickMe, CI/CD, TDD, DevOps, Terraform, Docker, Snowflake, PostgreSQL, AS400, Matillion, AWS (S3, ECS, ELB, Route53, ECR), sFTP, dbt, API, Step (Stibo), Linux, Bash scripting.

• Led migration of Vodafone’s Big Data platform to Google Cloud for all European markets handling terabytes per day
• Refurbished core data engineering squad capabilities after IR35 impact, assessing and mitigating technical debt
• Negotiated scope reduction with stakeholders to meet timelines and budget under COVID-19 constraints
• Delivered migration with minor delay despite loss of knowledge and missing documentation

Technologies: Java EE, Scala, Python, PySpark, GitHub, Jenkins, Jira, CI/CD, TDD/BDD, DevOps, test automation, load/stress testing, cost optimization, GCP (Dataflow, Composer, Dataproc, Cloud Storage, BigQuery, Bigtable, Spanner, Flink), Kubernetes, Docker, Terraform.

• Delivered “Project James,” a reinforcement learning AI for direct marketing optimization under a Google innovation grant
• Assessed a partially implemented platform, rebuilt the core using state-of-the-art tools, and tuned for production viability within schedule
• Overcame time pressure, partial documentation, and lack of prior case studies
• Revolutionized churn reduction and supported award-winning contact center

Technologies: Python, pandas, SciPy, NumPy, TensorFlow, Django, Flask, GitHub, Jenkins, Jira, GitOps, CI/CD, DevOps, Kubernetes, Docker, Terraform, microservices, Confluence.

• Managed delivery of a cloud logging and monitoring platform across 20-person, 3-site team for AWS public cloud adoption
• Reviewed architecture post-PoC and scaled the platform to handle 5TB/day (5 billion messages, 1.3 billion peak)
• Met strict cyber-security, availability, disaster recovery, and SLA/SLO requirements under constrained approved services
• Enabled monitoring of 5 mission critical cloud applications, pioneering new patterns and scalable architecture

Technologies: AWS (API Gateway, Route53, S3, DynamoDB, Kinesis, Elastic Beanstalk, Lambda, ELB, IAM, CloudWatch, CloudTrail), MySQL, Boto, Terraform, FluentD, Flink, Kafka, Kafka Streams, Kinesis Firehose, NiFi, Cassandra, CQL, Elasticsearch, Logstash, Kibana, Java EE, Python, Bitbucket, Jenkins, Jira, CI/CD, TDD, BDD, DevOps, Docker, Kubernetes, Datadog.

• Developed meta-analytics for the Corporate and Investment Bank, labeling and scoring all data repositories and software products
• Defined data quality metrics and formal ontologies for logical data models
• Scanned metadata to infer physical models and linked them via heuristics, with manual refinement by information architects
• Increased productivity of information architects by 4.7× through semi-automated processes

Technologies: Java, Spring, Python, RDF, OWL, semantic web standards, ontologies, knowledge graphs, BigQuery, ISO11179, Bitbucket, Jenkins, Jira, CI/CD, TDD, DevOps.