Data engineer
Resume examples

Wrote the most complex ETL (Extract / Transform / Load) processes, designs database systems, and developed tools for real-time and offline analytic processing. Troubleshoot software and processes for data consistency and integrity. Led the integration of highly complex and large-scale data from a variety of sources for business partners to generate insight and make decisions.

• Translated business specifications into design specifications and code.
• Established analytical rigor and methods for writing complex programs, ad hoc queries, and reports; ensured all code is structured and documented and is easy to maintain and reuse.
• Partnered with internal clients and leaders to gain an expert understanding of highly strategic, high-risk business functions and informational needs.
• Worked closely with technical and data analytics experts across the business to implement data solutions.
• Acted as the highest point of escalation for data analysis and served as a technical consultant for the client.
• Created an analytics-driven environment by gathering requirements, assessing gaps, and building roadmaps and architectures.
• Educated and developed junior data engineers on the team while applying quality control to their work and increasing their knowledge in specialized Data Engineering techniques and processes.
• Tested and implemented highly complex new software releases through regression testing. Identified issues and engaged with vendors to resolve and elevate software into production.
• Turned raw data into usable data pipelines and built data tools and products for effort automation and easy data accessibility.
• Diagnosed the existing architecture, data maturity, and identify gaps.
• Built data assets that enhance the quality of overall data structures by implementing the data science program.
• Identified gaps and implement solutions for data security, quality, and automation of processes.

Automated data ingestion procedures and improved overall reporting efficiency by 40% with the introduction of custom-built data views and reports. Collaborated with stakeholders and supervisor to define requirements and deliver tailored solution.

• Met with supervisor to outline process improvement strategy.
• Collaborated with engineering colleagues to develop and test algorithms designed to replace manual data ingestion, storage, and assessment operations.
• Liaised with stakeholders to provide updates and integrate feedback as needed.
• Obtained leadership approval and facilitated integration of new program utilizing SQL database and Google Analytics API technology.

Directed operations and supported companywide initiatives while leading team of 20 data engineers. Handled all aspects of performance management while championing continuous improvement.

• Delegated process development and database management tasks among engineers.
• Led interviews, onboarding, and training efforts to build and maintain a proficient development team while cultivating a collaborative and efficient work environment.
• Partnered with stakeholders to drive large-scale initiatives including migration to cloud-based integration software.

Designed and implemented real-time data pipeline to parse structured data from 25+ data sources using Kafka and PySpark applications. Utilized cross-functional collaboration to meet delivery deadlines.

• Gathered team of data engineers, programmers, and software architects.
• Strategized improved methods of collecting and storing large data sets.
• Researched, assessed, and selected third party program solutions for optimized integration and utilization.
• Trained department leaders and colleagues on how to integrate new applications and workflows for optimal results.