Data analyst
Resume examples

Assess key parameters using SQL and Python to drive product development with insightful analysis and collaboration. Maintain dashboards and data warehouses to quickly deliver information. Evaluate existing practices for efficacy and accuracy.

• Optimize existing Tableau SQL queries to process data for new product launches.
• Increase product sales 12% via market and exploratory analyses.
• Improve SPSS syntax execution by debugging outdated code and writing Python scripts to automate processes for integration with other applications.

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 three junior data analysts to process usable information and reported findings to appropriate managers and decision-makers. Helped to establish KPIs to evaluate the effectiveness of the company’s financial decisions, requiring the analysis of large data sets to find relevant information.

• Analyzed large and complex data sets to detect potential issues that could lessen productivity and provided deliverable actionable insights.
• Assisted in the development, design, and maintenance of four products of business at the company.
• Worked with product managers and engineers to translate the analysis into meaningful impact for the business.
• Identified actionable insights and made recommendations to project managers and cross-functional groups as directed by the supervisor.
• Supported payment risk control team by aggregating data from various sources to construct seamless information pipelines.

Tasked with transferring data into new formats to make the information more appropriate for analysis. Built analytical tools to automate the data collection process and decrease the amount of time for reporting and suggesting technological advancements and/or changes.

• Created and improved computer software and hardware under the direct supervision of the lead data scientist and computer engineers.
• Helped develop streamlined algorithms to reduce the amount of processing time and make computer tasks more efficient.
• Tested scalable schema designs, relational database, query performance, workflow optimization, and documentation.
• Developed significant and concise analytic objectives according to business goals and initiatives and under the guidance of department supervisors.
• Designed and built interactive dashboards, machine learning models, and innovative analytics tools using a variety of programming languages.

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.

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.

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.

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.