fernandob
Data Analytics Engineer
I work across analytics engineering, data engineering and data analysis, from ingestion and modelling to the metrics used for operational decisions. At Kraken, I spent six years in the team responsible for the customer support data platform, covering quality, documentation and reporting for chat and voice channels.
I took the initiative to set up the team's Google BigQuery environment. Later, we started using Kraken's data lake and took over pipelines built by the core engineering team. I built and maintained SQL and Python pipelines with Apache Airflow and worked with Apache Iceberg and Amazon Athena. I implemented dbt for our team within Kraken's existing shared platform, building dimensional models, tests and macros.
I worked directly with stakeholders to develop KPIs and dashboards used for performance, SLAs, workforce management, coaching and rewards. My work connected raw source data to the metrics people used for operational decisions.
I have been using AI daily since early 2024 and keep building tools around it, including MCP servers for Athena and QuickSight, a multi-LLM orchestration tool used mainly for code review, and a portable AI context system for sharing agent instructions across repositories. These tools were added to Kraken's AI tools catalogue.
In my personal data engineering lab, I build and run CDC, streaming and lakehouse systems.
Experience: 6 years
Yearly salary: $90,000
Hourly rate: $120
Nationality: 🇮🇹 Italy
Residency: 🇫🇷 France
Experience
Senior Data Analyst
Kraken 2020 - 2026
Over six years at Kraken, I grew from Data Analyst into Analytics Engineer and Data Engineer responsibilities within the customer support data team, covering ingestion, data modelling, quality, analysis and reporting. ・Built and maintained SQL and Python data pipelines for our customer support lakehouse on Apache Iceberg, using Docker, Apache Airflow and GitLab CI/CD. ・Implemented dbt for our team within Kraken's existing shared platform. Built several dozen models from staging to marts, with dimensional modelling, tests, macros, documentation, quality checks and access controls. ・Set up Kraken's first Google BigQuery environment, used in production for about three years. Built daily and hourly REST API ingestion with JavaScript/Apps Script, with IAM, service accounts, PII controls and monitoring. ・Developed KPIs, dashboards and business intelligence reporting for messaging and voice channels. Worked directly with stakeholders on performance, SLAs, workforce management, coaching and rewards. ・Reconstructed the chat acceptance rate from raw Zendesk and Playvox activity logs to account for omnichannel routing and staffing rules missing from vendor reports. ・Optimised SQL queries, partitioning and table properties on Iceberg/Amazon Athena to reduce run times and data scanned. Replaced full reloads with MERGE and targeted updates, and improved data freshness through batch scheduling. ・Published documentation and data lineage for the tables I owned in DataHub through dbt and GitLab. ・Built AI tools added to Kraken's AI tools catalogue: MCP servers for Athena queries and Amazon QuickSight charts, multi-LLM code review tooling, and shared agent instructions across repositories.
Skills
aws
big-data
ci-cd
dataops
docker
fintech
gcp
git
javascript
kyc
python
sql
data-science
english
french
italian
portuguese
spanish