- Developed and maintained data pipelines using SQL, Python, PySpark, Airbyte, and DBT to streamline data processing and analysis;- Implemented cloud-based solutions on - AWS (Glue, Delta Lake, S3, Lambda, Athena, MWAA Airflow), - GCP (BigQuery, Cloud Functions, GCS, Pub Sub, Cloud SQL), - MongoDB Atlas and Delta Lake for efficient data storage and retrieval;- Orchestrated the integration of sources, including Facebook, Google Analytics, and HubSpot, into a data lake, enhancing marketing data;- Incorporating various reports such as Sales, Asset Yield, Revenue Yield, Historical Subscription, and Vacancy to enhance stakeholder decision-making processes;- Provided internal training capacity to enhance data literacy and foster a data-driven culture.- I actively contributed to the modeling new back-office data structures to facilitate efficient data storage, retrieval, and analysis.- Experienced data professional proficient in leveraging Airbyte for seamless data integration and DBT for transformative data modeling.- Skilled in data visualization tools including Tableau and Looker, to deliver actionable insights

- Management of the existing and evolution of the Data Lake infrastructure and data pipelines.- Working with the Data Engineering team to develop queries/pipelines for data extraction from datastore, APIs, and databases;- Delivered complex ad-hoc analysis with BigQuery;- Working with Google Cloud Platform developing and managing the following services: - BigQuery, Dataflow, Dataprep, Google Cloud Storage, Pub/Sub, Cloud Functions, Google Composer, Google Kubernetes Engine, StreamSets, and Rundeck.

- Implemented and utilized Kafka, Spark Structured Streaming, and NIFI to enable efficient data processing and streaming capabilities, ensuring real-time data availability and enabling seamless data flow.- Leveraged Cloud Service Azure, including Data Lake Gen2, Azure Functions using Python, and Azure DevOps, to design and implement scalable and reliable data solution- Employed advanced optimization techniques to enhance data pipeline performance, improving data flow efficiency and reducing processing time

- Managed data stacks on Amazon Web Services including EMR, DynamoDB, RedShift, S3, Glue, Kinesis, and Elasticsearch; - Implemented the process of data democratization through the Dremio’s Data Lake Engine; - Collaborated across departments to implement GDPR procedures, ensuring data integrity and data governance; - Developed ELT pipelines for data extraction from applications like Sinacor, Bcart, and Virtual Open, including SQL Server, MySQL, and Oracle. - Provided mentorship and support to junior colleagues.; - Developed Python and shell script routines to automate processes and provide data observability and data flow; - Built batch data pipelines using Airflow for decreased operational toil; - Refactored SQL queries, reducing fetching time by 38%;- Maintaining supporting documentation for designing and building data-related processes.

- Managed databases and data sources including SQL Server, MySQL, and MariaDB across 1000+ instances; - Leveraged SQL Server (OLAP), SQL, SSIS, SSRS, and related data technologies; - Crafted complex SQL queries and optimized performance through refactoring; - Developed Python scripts for data extraction and processing; - Created Python data pipelines to streamline data delivery to the BI team; - Applied database concepts to materialize business requirements;

- Managed data sources including SQL Server, Oracle (OLAP and OLTP versions). - Proficient in Linux, SQL, and shell scripting. - Developed Python scripts for data extraction and processing; - Implemented data resiliency strategies using SQL Server replication and Golden Gate to ensure high availability and minimize downtime. - Leveraged JMS resources in WebLogic and IIS for seamless data sharing and communication, optimizing data flow and integration. - Successfully led strategic projects, including migrations: - from Oracle Golden Gate 11 to Oracle Golden Gate 12, - from IBM Maximo 7.5 to IBM Maximo 7.6, - and from IBM Maximo 7.1 to IBM Maximo 7.6 platforms.