Data Engineer with over 10 years of experience in data pipelines, data warehousing, and analytics platforms. Proven ability to design and optimize ETL/ELT workflows, manage large-scale data processing, and implement modern data stack tools like DBT, Airflow, BigQuery, and Snowflake. Strong background in BI and performance tuning of data flows. Passionate about automation, data quality, and efficient data modeling using star and snowflake schemas.
Built a robust ETL pipeline in Pentaho PDI, parameterized with database-driven variables.
Enabled multi-environment execution with a single pipeline, eliminating duplicated flows.
Added automated reprocessing and modular activation/deactivation via control tables.
Established a new development standard, improving agility, consistency, and maintenance efficiency.
Developed a Python script integrated with Slack for data validation.
Supported both record count validation (source vs. target) and anomaly detection (out-of-threshold values).
Allowed execution on schedule or on demand via Slack commands.
Persisted validation results for dashboard monitoring, reducing support tickets and boosting client trust in ETL outputs.
Designed and implemented an ETL integration of client-provided SellOut data with platform data.
Automated file handling: bucket monitoring, control table logging, processing, validation, alerts, and cleanup.
Consolidated processed data into the client's existing data model and delivered daily invoice reports.
Leveraged Python, AWS S3, MySQL, Pentaho, and Snowflake to ensure reliability and automation.
Developed a data model to represent media campaign structures, enabling cohort and seasonality analysis.
Using this model, implemented a linear regression in Tableau to forecast break-even points for media campaigns, supporting more data-driven marketing strategies.
Created a financial report integrating multiple data sources (Excel, databases, and internal systems) to automate the full chain of tax and levy calculations, generating a profit/loss view by service line.
This report provided the Finance team with greater agility - allowing them to focus on results instead of manual data aggregation - and gave management better visibility into service profitability.
Designed and implemented a Star Schema data model for a client in the insurance sector.
The model was fed daily via full-dimension files and incremental fact files, and also served as the underlying data source for Tableau dashboards, improving accessibility and reporting consistency.
Developed ETL routines for automated data validation, integrated as mandatory steps in the daily orchestration process.
These validations increased trust in the generated data and reduced the number of support tickets that clients opened.
Created a data model to store user subscription history from media campaigns.
Before a new subscription was confirmed, the system validated whether the user had previously subscribed and applied predefined payment rules to decide acceptance.
This solution improved media campaign performance by 3.5%, optimizing acquisition costs and campaign ROI.
Enabled multi-environment execution with a single pipeline, eliminating duplicated flows.
Added automated reprocessing and modular activation/deactivation via control tables.
Established a new development standard, improving agility, consistency, and maintenance efficiency.
Supported both record count validation (source vs. target) and anomaly detection (out-of-threshold values).
Allowed execution on schedule or on demand via Slack commands.
Persisted validation results for dashboard monitoring, reducing support tickets, and boosting client trust in ETL outputs.
Automated file handling: bucket monitoring, control table logging, processing, validation, alerts, and cleanup.
Consolidated processed data into the client's existing data model and delivered daily invoice reports.
Leveraged Python, AWS S3, MySQL, Pentaho, and Snowflake to ensure reliability and automation.
Implemented a linear regression in Tableau to forecast break-even points for media campaigns, supporting more data-driven marketing strategies.