Tamara is available for hire
Hire TamaraTamara Sente
Verified Expert in Engineering
Data Pipelines Developer
Zagreb, Croatia
Toptal member since June 12, 2026
Bio
Tamara is a senior data engineer with 10 years of experience, and an expert in Databricks, PySpark, and multi-cloud data platforms across fintech, security and intelligence, and AI domains. She's delivered 2TB zero-downtime migrations, 30% BigQuery cost reductions, and 7-hour to under 2-hour reporting latency improvements. A technical lead with a track record in engineering standards and mentoring teams, she builds scalable ETL/ELT, medallion architectures, CDC streaming, and LLM/RAG pipelines.
Portfolio
Aisma via Ignotitia
Python, PySpark, Databricks, Pulumi, Azure, Azure Data Factory (ADF), GitHub...
British Petroleum via Ignotitia
Databricks, Unity Catalog, Medallion Architecture, CI/CD Pipelines, SFTP...
Minka
BigQuery, MySQL, PostgreSQL, CDC, Debezium, Pub/Sub, Python, Kubernetes, GitHub...
Experience
- SQL - 10 years
- Data Pipelines - 10 years
- Data Modeling - 10 years
- Python - 5 years
- Medallion Architecture - 4 years
- PySpark - 4 years
- Databricks - 4 years
- CI/CD Pipelines - 3 years
Preferred Environment
Databricks, PySpark, SQL, Azure, Medallion Architecture, Python, Data Architecture, Data Pipelines, Data Modeling, ETL
The most amazing...
...thing I've built is a zero-downtime pipeline migrating 2TB of live financial data from MySQL to PostgreSQL using CDC streaming.
Work Experience
Senior Data Engineer
2024 - PRESENT
Aisma via Ignotitia
- Refactored Python ingestion pipelines into production PySpark on Databricks, achieving a 1,000x performance improvement for a fintech startup.
- Designed ingestion, transformation, and indexing pipelines delivering trusted datasets for LLM/RAG ML workflows.
- Built infrastructure-as-code for Databricks platform deployments using Bundles and Pulumi.
- Built scalable batch and real-time processing pipelines for large-scale geospatial datasets.
- Ensured data integrity and quality across multiple sources in collaboration with DS/ML teams.
Technologies: Python, PySpark, Databricks, Pulumi, Azure, Azure Data Factory (ADF), GitHub, Data Architecture, Terraform, Git, Vector Indexing, Delta Lake, Spark SQL, Apache Spark, Cloud, Azure Data Lake, Data Engineering, Data Cleaning, Data Lake Design
Senior Data Engineer
2023 - 2026
British Petroleum via Ignotitia
- Architected the data pipeline architecture end-to-end using Databricks Unity Catalog, Delta Live Tables, and medallion architecture.
- Owned data model design across multiple greenfield client data platform projects.
- Optimized data workflows for performance, reliability, and cost efficiency.
- Established CI/CD deployment standards and monitoring for Databricks and ADF pipelines across client environments.
- Integrated diverse structured and unstructured sources (SFTP, REST APIs, SQL DBs, Microsoft Graph, ArcGIS, PDFs) into governed data lakes; enforced data integrity, quality, and security best practices.
- Led and mentored teams of up to 4 engineers. Collaborated cross-functionally with software engineers, data analysts, and product owners to deliver end-to-end data solutions.
Technologies: Databricks, Unity Catalog, Medallion Architecture, CI/CD Pipelines, SFTP, REST APIs, SQL Server, Data Architecture, Git, Azure Monitor, Azure DevOps, Azure Data Factory (ADF), Delta Live Tables (DLT), Delta Lake, Spark SQL, Azure, SQL, CDC, Apache Spark, Cloud, Azure Data Lake, Data Engineering, Data Cleaning, Data Lake Design
Data Engineer
2021 - 2023
Minka
- Architected and maintained an analytical data warehouse using SCD Type 2 logic. Reduced BigQuery costs by 30%. Improved data availability from 7 hours to under 2 hours.
- Led a zero-downtime migration of 2TB of data from MySQL to PostgreSQL using CDC-Debezium, Pub/Sub, Python, and Kubernetes with a real-time streaming approach on GCP.
- Contributed to data team growth through technical interviewing and candidate evaluation.
- Ensured data integrity, quality, and validation across operation vs. analytics layers.
- Handled reconciliation monitoring and stakeholder reporting reliability in a fintech data product environment.
Technologies: BigQuery, MySQL, PostgreSQL, CDC, Debezium, Pub/Sub, Python, Kubernetes, GitHub, Docker, Git, Looker, Google Cloud Platform (GCP), Cloud, Data Engineering, Data Cleaning, Data Lake Design
Senior DWH Consultant/Engineer
2018 - 2021
INTESA SANPAOLO via Neos
- Designed, implemented, and optimized ETL pipelines and DWH architecture and data models using Oracle Data Integrator and Oracle DB for financial reporting and regulatory compliance.
- Collaborated closely with the business on data definitions and quality standards. Collaborated with Data Governance to maintain strict data quality standards. Mentored junior engineers.
- Unified disparate data sources across group entities into a single DWH.
- Automated advanced SQL queries for daily financial reporting, achieving 10x speed improvement. Optimized reconciliation logic execution time by 5x.
Technologies: ETL, Oracle Database, SQL, Oracle, PL/SQL, Data Engineering, Data Cleaning
Junior Data Scientist
2017 - 2018
Gas Distribution System Zagreb via Neos
- Developed an ML forecasting model in R, predicting hourly natural gas consumption 24 hours ahead.
- Deployed the forecasting model into a production energy sector environment where predictions directly informed operational decisions on gas distribution and supply planning.
- Trained models on historical consumption data and validated against real production measurements across the distribution network. Iterated on feature selection and model parameters to improve forecast accuracy before production deployment.
Technologies: R, Oracle, Data Cleaning
Data Scientist
2016 - 2017
Axilis
- Built a client segmentation model using clustering algorithms in R and MongoDB that grouped customers by behavior and purchasing patterns, enabling targeted marketing campaigns for an eCommerce client.
- Designed and deployed a collaborative filtering recommendation system in Python that personalized product suggestions for active webshop customers based on purchase history and browsing behavior.
- Delivered end-to-end data science solutions from raw data ingestion through model deployment, owning the full pipeline from data cleaning and feature engineering to production deployment and monitoring.
Technologies: R, MongoDB, Python, Git, Data Cleaning
Data Science Intern
2016 - 2016
Crossing Technologies
- Built a sentiment analysis pipeline ingesting data from Twitter and Facebook APIs, processing thousands of social media comments per hour to extract public opinion signals.
- Applied NLP classification models to social network data to predict the outcome of the 2016 US presidential election; model prediction proved correct on election night.
- Built and labeled a training dataset for the Croatian language.
Technologies: Python, Data Cleaning
Experience
CI/CD Pipeline for Databricks AI Data Platform
I designed and implemented end-to-end CI/CD pipelines for Databricks AI data platform deployments across multiple client engagements, eliminating manual deployments and establishing reproducible, auditable release processes across dev, staging, and production environments.
I used Databricks Asset Bundles and Pulumi to define the entire platform configuration as code—jobs, clusters, Unity Catalog objects, and permissions—all version-controlled alongside application code. I also managed Databricks workspaces and ADLS Gen2 storage.
The CI pipeline validated the bundle configuration, ran Python unit tests against the transformation logic, and checked code quality standards for every pull request. The CD pipeline deployed bundles to staging automatically on merge, ran integration tests against live Databricks workflows, and required a manual approval gate before promoting to production.
Stack:
Databricks Asset Bundles, Pulumi, GitHub, GitHub Actions, Python, Unity Catalog, Delta Live Tables, Azure
I used Databricks Asset Bundles and Pulumi to define the entire platform configuration as code—jobs, clusters, Unity Catalog objects, and permissions—all version-controlled alongside application code. I also managed Databricks workspaces and ADLS Gen2 storage.
The CI pipeline validated the bundle configuration, ran Python unit tests against the transformation logic, and checked code quality standards for every pull request. The CD pipeline deployed bundles to staging automatically on merge, ran integration tests against live Databricks workflows, and required a manual approval gate before promoting to production.
Stack:
Databricks Asset Bundles, Pulumi, GitHub, GitHub Actions, Python, Unity Catalog, Delta Live Tables, Azure
Education
2014 - 2016
Master's Degree in Mathematical Statistics
University of Zagreb - Zagreb, Croatia
2011 - 2014
Bachelor's Degree in Mathematics
University of Zagreb - Zagreb, Croatia
Certifications
JUNE 2025 - JUNE 2027
Data Engineer Associate
Databricks
OCTOBER 2018 - PRESENT
Oracle PL/SQL Developer Certified Associate
Oracle
Skills
Libraries/APIs
PySpark, REST APIs
Tools
BigQuery, Spark SQL, Git, GitHub, Azure Monitor, Looker, Terraform
Languages
SQL, Python, R
Frameworks
Apache Spark, Delta Live Tables (DLT)
Paradigms
ETL, Azure DevOps
Platforms
Databricks, Oracle Database, Azure, Oracle, Debezium, Kubernetes, Google Cloud Platform (GCP), Docker
Storage
Data Pipelines, Data Lake Design, MySQL, PostgreSQL, PL/SQL, MongoDB
Other
Unity Catalog, Medallion Architecture, Data Modeling, Cloud, Data Engineering, Data Cleaning, CI/CD Pipelines, SQL Server, CDC, Delta Lake, Azure Data Factory (ADF), Data Architecture, Azure Data Lake, Pulumi, SFTP, Pub/Sub, Data Build Tool (dbt), Vector Indexing, Statistics, Mathematics, Analytics
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