Data analyst
Resume examples

Develop insights and data visualizations to maximize opportunity and increase efficiency. Prepare monthly reports for management and key stakeholders. Translate complex data models to understandable written narratives for ad hoc information requests.

• Lead a junior data analyst team to synthesize data driven reports and present findings interdepartmentally.
• Optimize Tableau dashboard systems through outdated query removal and REGEX string implementation.
• Develop and standardize data cleaning procedures to validate accuracy using both Microsoft Excel and Tableau Prep.

Developed queries and performed extensive programming to access, transform, and prepare data for statistical modeling. Structured and translated requirements into an analytic approach.

• Conducted deep-dive diagnostic, predictive, and prescriptive analytics to support data-driven business decision-making.
• Identified and diagnosed data inconsistencies and errors, documented data assumptions, and forages to fill data gaps.
• Engaged with internal stakeholders to understand and probe business processes to develop hypotheses.
• Guided test design, research design, and model validation.
• Served as the analytics expert and statistical consultant to cross-functional teams for large strategic initiatives and contributed to the growth of the company’s analytic community.
• Delivered insight presentations and action recommendations.
• Communicated complex analytical findings and implications to business.

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.

Collaborated with data analytics team members to extract information, facts, and figures from numerous sources. Monitored key performance indicators and created reports to help decision-makers determine and evaluate the company’s progress as related to defined initiatives and goals.

• Applied technological insight and creativity to assist the business department to create and maintain scalable solutions and automated reporting processes.
• Recommended improvements in the tools and techniques in use to help scale the team’s overall productivity and success.
• Developed a deep understanding of business payments and website functionality which further strengthened analytical presentation skills.
• Identified key metrics and built C-level dashboards to track the progress of the company and its highest priority initiatives.
• Work collaboratively with cross-functional teams including Finance, Marketing, Product Development, and Engineering.

Explored the fundamental issues in computing and developed theories, models and processes to address those issues. Assisted lead data scientists and engineers to solve complex analytical problems. Played an integral role in developing new computing languages to improve the way users interact with information.

• Improved the efficacy of reporting and disseminating data findings to key decision-makers by revamping the data visualizing process.
• Researched and analyzed business needs and made recommendations to upgrade complex applications, system administration issues, and network concerns.
• Prepared and maintained technical data, reports, documentation, and briefings and created presentations to communicate information to various departments.
• Transformed user requests and requirements into technical design specifications and served as a liaison between all involved parties.
• Performed systems management and integration functions by reviewing system capabilities, workflow, and schedule limitations.

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.

Conducted full lifecycle analyses and reports on multiple sub-projects for a $2M CDC grant. Monitored performance and created reports/presentations based on findings and peer recommendations. Utilized infographics, graphs, and a variety of other methods to visualize data for key stakeholders.

• Analyze websites to ensure consistent site traffic and conversion rates; make recommendations to optimize products and features.
• Review visitor behavior and translate data results into actionable items that help drive business initiatives and improve company performance.
• Created and implemented data structures and pipelines to collect, organize, and standardize information to prepare business insights.
• Developed and automated analytical reports for various departments and key stakeholders throughout the company.
• Established and maintained strong relationships with internal teams and provided data analysis and support as necessary.

Delivered ML project to customers from conception to completion by overseeing business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining. Utilized AWS AI services (Personalize), ML platforms (SageMaker), and frameworks MXNet, TensorFlow, PyTorch, SparkML, scikit-learn) to help customers build ML models.

• Researched and implemented novel ML approaches, including hardware optimizations on platforms such as AWS Inferentia.
• Worked with Professional Services consultants (Big Data, IoT, HPC) to analyze, extract, normalize, and label relevant data to operationalize customers’ models after they are prototyped.
• Wrote and delivered data-driven presentations about technical concepts to business, technical, and lay audiences.

Saved enterprise technology company $900K+ in expenses over 1-year timeframe via migration initiative. Oversaw project planning, database integration, and engineering operations as project leader.

• Petitioned transfer to more streamlined database management software to senior leadership for approval.
• Held strategy meetings to define requirements and outline migration steps.
• Facilitated data transfer tasks from enterprise system to MongoDB cloud-based solution for improved accuracy, efficiency, and storage capacity.
• Drafted and distributed database management guidelines and best practices.