Data Engineer III with experience in data engineering, ETL, data pipelines, QA automation, and cloud-based data infrastructure. Experienced in Python, SQL, SAS, DBT, AWS, Snowflake, BigQuery, Git, Jira, Looker, Tableau, …
Data Engineer III with experience in data engineering, ETL, data pipelines, QA automation, and cloud-based data infrastructure. Experienced in Python, SQL, SAS, DBT, AWS, Snowflake, BigQuery, Git, Jira, Looker, Tableau, and Airflow, with a background in machine learning and predictive modeling.
+ Collaborated with a small team to create and manage 16 different data pipelines and provide clean real-time
inventory and financial data to the analytics and executive team.
+ Successfully refactored 16 diverse data pipelines within a defined time-frame, transitioning from an obsolete CDC
platform to a new CDC solution
+ Implemented automated alerts and standardized error-handling protocols, enhancing data quality and pipeline
efficiency.
+ Ensured data quality and pipeline efficiency by implementing automated alerts and standard procedures for error
resolution.
+ Collaborated cross-functionally via Git, conducting rigorous code reviews and ensuring version control alignment.
+ Leveraged DBT to stage raw data, optimizing data warehouse utilization and scheduling Looker models for real-time
insights.
+ Used Jira to facilitate seamless project management and stakeholder engagement, fostering cohesive communication
channels.
<p>Collaborated with a small team to create and manage 16 different data pipelines and provide clean real-time inventory and financial data to the analytics and executive team.<br>
Successfully refactored 16 diverse data pipelines within a defined time-frame, transitioning from an obsolete CDC platform to a new CDC solution.<br>
Implemented automated alerts and standardized error-handling protocols, enhancing data quality and pipeline efficiency.<br>
Ensured data quality and pipeline efficiency by implementing automated alerts and standard procedures for error resolution.<br>
Collaborated cross-functionally via Git, conducting rigorous code reviews and ensuring version control alignment.<br>
Leveraged DBT to stage raw data, optimizing data warehouse utilization and scheduling Looker models for real-time insights.<br>
Used Jira to facilitate seamless project management and stakeholder engagement, fostering cohesive communication channels.</p>
<p>Led data engineering and development efforts to clean and integrate disparate data sources, providing efficient and scalable automation solutions to Merkle’s fastest growing client.<br>
Automated QA for each step of the ETL process, detecting and alerting potential errors in real-time.<br>
Parsed highly variable client data and designed infrastructure to append multiple sources of data to the customer file.<br>
Successfully migrated data infrastructure from legacy solutions to cloud-based solutions.<br>
Implemented predictive models and performed monthly model scoring using evolving data sets.<br>
Optimized ETL procedures and QA, including overhauling legacy code to reduce a monthly computation time from 6 days to 2 hours.</p>
<p>Developed and maintained data pipelines from multiple sources, cleaning and processing data sets containing up to 250 million observations.<br>
Leveraged supervised and unsupervised machine learning techniques to build customer profiles and generate predictive models for response rates, providing actionable insights to the team.</p>
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