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Blake Sterling - Senior Data Analyst

[email protected]

Location: Leander, Texas, USA

Relocation: Yes

Visa: USC

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Blake Sterling Senior Data Analyst Email: [email protected] Texas, United States Professional Summary Senior Data Analyst with 7+ years of experience across banking, healthcare, telecommunications, and financial services, specializing in financial analytics, data analysis, reporting, forecasting, and enterprise data solutions. Strong background in analyzing financial, operational, transaction, claims, risk, and performance datasets using SQL, Python, Excel, and modern BI platforms to deliver reliable business insights. Experienced in building and maintaining Power BI and Tableau dashboards, semantic models, DAX measures, KPI frameworks, and executive reporting solutions for Finance, Risk, Operations, and business leadership. Hands-on expertise in SQL-based data analysis, including complex joins, CTEs, aggregations, window functions, stored procedures, data validation, reconciliation, and performance optimization. Proven experience supporting budgeting, forecasting, P&L analysis, variance analysis, scenario planning, financial modeling, cost analysis, and management reporting across enterprise business units. Experienced with cloud-based data platforms and pipelines including Azure Data Factory, Azure Databricks, Azure SQL, ADLS Gen2, AWS Glue, Amazon S3, Amazon Redshift, and Microsoft Fabric. Strong understanding of ETL/ELT, data transformation, data quality, data governance, data lineage, source-to-target mapping, reconciliation, and multi-layer data architectures. Worked with sensitive and regulated datasets across banking and healthcare, supporting regulatory reporting, data controls, audit readiness, healthcare interoperability, and enterprise governance requirements. Proficient in Python and R for exploratory analysis, statistical analysis, automation, data transformation, trend analysis, and root-cause investigations, with additional experience using Airflow, dbt, SSIS, and Alteryx. Collaborative and ownership-driven analyst who works closely with Finance, Risk, Product, Engineering, Operations, QA, Compliance, and business stakeholders to translate requirements into scalable analytical solutions and deliver accurate insights within business and regulatory timelines. SKILL Category Technologies & Skills Programming & Analysis Python, SQL, R, Pandas, Scikit-learn BI & Visualization Power BI, Tableau, DAX, MicroStrategy, KPI Dashboards, Executive Reporting Data Engineering ETL/ELT, Data Integration, Data Transformation, Data Validation, Data Reconciliation, Data Quality Cloud & Data Platforms Azure, Azure Data Factory, Azure Databricks, Azure SQL, ADLS Gen2, Microsoft Fabric, AWS Glue, Amazon S3, Amazon Redshift Databases Oracle, SQL Server, Azure SQL, Amazon Redshift Data Modeling Star Schema, Dimensional Modeling, Semantic Models, Facts, Dimensions, Data Lineage, Source-to-Target Mapping Financial Analytics Financial Modeling, Budgeting, Forecasting, P&L Analysis, Variance Analysis, Cost Analysis, Scenario & What-If Analysis, NPV, IRR Risk & Regulatory VaR, Stress Testing, RWA, Basel III, CCAR, Capital Ratio Analysis, Regulatory Reporting, Audit Readiness Data Pipelines & Automation Apache Airflow, dbt, SSIS, Alteryx, Alteryx Server, Power Query, VBA, Python Automation Healthcare Data EHR, Claims, HL7 v2, FHIR R4, CCDA, X12, REST APIs, JSON, SMART on FHIR Development & Collaboration Git, Jira, Confluence, Agile/Scrum, UAT, Regression Testing, L2/L3 Production Support Bank of America Senior Data Analyst (2025 - Present) | Remote Own enterprise-level financial and data analytics initiatives across finance, business planning, forecasting, and data governance, working with large banking datasets to improve data quality, reporting consistency, and decision-making across business portfolios. Lead revenue, expense, and financial forecasting by building driver-based models in Python, SQL, Advanced Excel, Power Query, and Power Pivot, incorporating historical trends, seasonality, business drivers, and scenario analysis to improve forecast accuracy by approximately 20%. Analyze P&L, budget, forecast, and actual results across multiple cost centers, performing detailed variance and root-cause analysis to identify spending trends, cost leakage, and budget risks and providing Finance leadership with actionable recommendations before month-end close. Build and maintain automated ETL and data preparation workflows using SQL, SSIS, Alteryx, Airflow, and dbt, integrating financial, customer, account, transaction, payment, and operational datasets from multiple enterprise sources into trusted reporting layers. Develop complex SQL queries, stored procedures, reusable SQL views, and data validation scripts across Oracle and SQL Server, supporting data cleansing, transformation, reconciliation, and reporting while reducing report-generation time by approximately 30%. Strengthen enterprise data governance and data quality by defining validation rules, source-to-target mappings, data ownership requirements, and lineage documentation; implemented checks for record counts, duplicates, nulls, rejected records, and business-rule exceptions, reducing data discrepancies by approximately 15%. Support regulatory and management reporting by validating datasets across bronze, silver, and gold data layers in Azure Data Lake Storage Gen2, ensuring consistent customer, account, transaction, payment, loan, and KPI data as it moves from source systems through curated and reporting layers. Develop executive-level Power BI and Tableau dashboards covering financial performance, revenue, expenses, liquidity, sales, product, and operational KPIs, combining multiple data sources and automated refreshes to improve leadership visibility and accelerate decision-making. Design and maintain MicroStrategy schemas and reporting models using attributes, facts, metrics, and dimensional structures through MicroStrategy Architect and Developer, ensuring reporting logic remains consistent with governed enterprise data definitions. Automate recurring reporting and reconciliation processes using Excel VBA, Power Query, Python, SQL, and Alteryx, eliminating approximately 30 35% of manual reporting effort, improving accuracy, and shortening monthly reporting cycles. Manage and optimize Alteryx workflows and Alteryx Server deployments, automating repetitive data preparation and reconciliation activities while scheduling reliable data processing for downstream reporting and analytics teams. Partner with Finance, Treasury, Sales, Marketing, Product, Operations, IT, QA, and Data Engineering teams to gather requirements, translate business needs into analytical solutions, define KPIs, resolve data issues, and deliver reporting products aligned with governance standards. Provide L2/L3 production support for Tableau, Power BI, MicroStrategy, Alteryx, and data pipelines, investigating failed loads, refresh issues, missing records, and source-to-report discrepancies while maintaining 99%+ reporting reliability and meeting established SLAs. Support AOP, quarterly forecasting, rolling forecasts, budgeting, and $10M+ investment analysis by developing scenario-based financial models and performing NPV, IRR, sensitivity, and cost analysis to help stakeholders evaluate financial performance, risk, and investment decisions. Environment: Python, SQL, Oracle, SQL Server, SSIS, Alteryx, Alteryx Server, Airflow, dbt, Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Data Factory (ADF), Power BI, Tableau, MicroStrategy, MicroStrategy Architect, MicroStrategy Developer, Advanced Excel, VBA, Power Query, Power Pivot, ETL/ELT, Data Validation, Data Quality, Data Governance, Data Lineage, Source-to-Target Mapping, Data Reconciliation, Financial Modeling, Forecasting, Budgeting, Variance Analysis, P&L Analysis, KPI Reporting, Regulatory Reporting, Scenario Analysis, NPV, IRR, Sensitivity Analysis, L2/L3 Production Support. HCA Healthcare Financial Data Analyst (2023 - 2025) | Nashville, TN Owned financial and operational data analysis initiatives supporting healthcare finance, claims, clinical operations, and business reporting, working with large EHR, claims, membership, provider, and clinical datasets to improve reporting accuracy and operational visibility. Built and monitored automated ELT/ETL pipelines using Azure Data Factory and AWS Glue, integrating data from enterprise healthcare systems, Azure Blob Storage, EHR platforms, claims sources, and CCDA feeds into centralized analytical and reporting environments. Performed detailed financial and operational analysis using SQL and Python, analyzing utilization, claims, cost, revenue, and operational trends to identify variances, emerging patterns, and opportunities for improved business planning. Developed complex SQL queries, joins, aggregations, window functions, reusable views, and reconciliation scripts to validate healthcare and financial datasets, identifying missing, duplicate, inconsistent, and rejected records before they reached downstream reporting. Created and maintained Power BI and Tableau dashboards for finance, clinical, and operational leadership, developing KPI calculations, DAX measures, dimensional models, and interactive reporting that improved reporting efficiency by approximately 30%. Developed reusable Python data-transformation and automation scripts to standardize healthcare datasets, automate recurring reporting activities, and reduce manual data-preparation effort by approximately 40% while maintaining consistent analytical outputs. Supported healthcare interoperability and financial data exchange initiatives involving HL7 v2, HL7 FHIR R4, CCDA XML, X12, REST APIs, JSON, and SMART on FHIR, validating data transformations across claims, membership, provider, and clinical domains. Designed and validated CCDA-to-FHIR mappings for resources including Patient, Encounter, Observation, Claim, and Coverage, reviewing field-level transformations, business rules, data types, and downstream impacts to ensure accurate movement of healthcare information. Partnered with Finance, Clinical Operations, Product, Engineering, Architecture, QA, Compliance, and Data teams to gather requirements, document source-to-target mappings, functional requirements, API specifications, process flows, acceptance criteria, and data-validation rules. Supported data governance and regulatory reporting by implementing SQL/Python data-quality checks, source-to-target reconciliation, data lineage documentation, and Azure role-based access controls while maintaining appropriate safeguards for sensitive healthcare information. Provided production and integration support by troubleshooting ADF and AWS Glue pipeline failures, FHIR API issues, HL7 message errors, dashboard refresh problems, and data discrepancies, performing root-cause analysis and coordinating fixes through Jira and Confluence. Participated throughout the Agile delivery lifecycle, including requirements workshops, backlog refinement, sprint planning, UAT, integration validation, regression testing, defect triage, production readiness, and post-release support, acting as the bridge between business stakeholders and technical teams. Environment: SQL, Python, Azure Data Factory (ADF), Azure SQL Database, Azure Blob Storage, AWS Glue, Power BI, Tableau, DAX, Dimensional Modeling, ETL/ELT, Data Profiling, Data Validation, Data Reconciliation, Data Quality, Data Mapping, Data Lineage, EHR, Claims Data, HL7 v2, HL7 FHIR R4, CCDA XML, X12, SMART on FHIR, REST APIs, JSON, FHIR APIs, Smile CDR, Jira, Confluence, Azure RBAC, Agile/Scrum, UAT, Regression Testing, Root-Cause Analysis. JPMorgan Chase Financial Data Analyst (2021 2023) | Plano, TX Owned financial data analysis and business intelligence initiatives supporting banking and finance teams, working with high-volume transaction, customer, account, and operational datasets to improve reporting accuracy, data transparency, and decision-making. Designed a Star Schema data model in Amazon Redshift to analyze and audit more than 2M financial transactions, establishing a reliable single source of truth for downstream financial reporting and reconciliation. Developed and maintained automated ETL pipelines using AWS Glue, Amazon S3, SQL, and Apache Airflow, integrating data from multiple banking systems and reducing approximately 15 hours of manual data-preparation work each week. Used SQL and Azure-based data platforms for complex data extraction, transformation, validation, and analysis, building reusable queries and data-processing logic for recurring financial reporting requirements. Worked with Azure Data Factory and Azure Databricks to integrate and process enterprise datasets, supporting scalable transformation workflows and improving the reliability of downstream analytics. Built executive-facing Power BI and Tableau dashboards covering more than 15 high-priority financial and operational KPIs, enabling Finance leadership to monitor performance, perform gap analysis, and identify business trends more efficiently. Developed advanced DAX calculations, measures, KPIs, and reporting logic in Power BI while optimizing dashboard queries and data models to improve report responsiveness and usability for business stakeholders. Performed detailed data validation, reconciliation, and quality checks across source and reporting datasets, investigating mismatched records, missing values, duplicate transactions, and unexpected financial movements before reports were distributed. Built Python-based analytical workflows using Pandas and Scikit-learn to analyze more than 500K records, identifying over 200 irregular financial patterns and providing additional insights for operational-risk monitoring. Automated recurring reporting processes using Apache Airflow and SQL, reducing manual dependencies and improving reporting turnaround across five global business units while ensuring scheduled data was available for business reviews. Optimized Amazon Redshift and AWS Glue data-processing and query workloads by reviewing data models, transformation logic, and storage patterns, reducing processing overhead and saving more than 10 hours of processing time per week. Worked closely with Finance, business users, data engineers, QA, and technical teams to gather requirements, translate business needs into reporting solutions, troubleshoot production issues, document workflows in Confluence, and deliver scalable analytics within Agile delivery cycles. Environment: SQL, Python, Pandas, Scikit-learn, Power BI, Tableau, DAX, Amazon Redshift, AWS Glue, Amazon S3, Apache Airflow, Azure Data Factory (ADF), Azure Databricks, Azure, ETL/ELT, Data Modeling, Star Schema, Dimensional Modeling, KPI Reporting, Financial Reporting, Data Validation, Data Reconciliation, Data Quality, Dashboard Optimization, Transaction Analysis, Anomaly Detection, Reporting Automation, Confluence, Agile/Scrum. AT&T Data Analyst (2020 2021) | Remote Owned end-to-end data analysis and reporting for Finance, FP&A, and business teams, working with large financial and operational datasets to identify trends, explain performance gaps, and turn business requirements into actionable insights. Used advanced SQL to extract, join, clean, and analyze large datasets using CTEs, aggregations, subqueries, and window functions, improving data retrieval and analytical efficiency by approximately 25%. Performed Budget vs. Actual, Forecast vs. Actual, and Prior-Year variance analysis across multiple departments, identifying cost drivers, spending patterns, and financial performance gaps that contributed to approximately 15% improvement in planning and management reporting. Built interactive Power BI and Tableau dashboards covering financial KPIs, budget utilization, expense trends, profitability, operational performance, and forecast metrics, reducing manual reporting effort by approximately 30% and giving stakeholders better visibility into business performance. Developed Power BI semantic models, DAX measures, calculated columns, and star-schema data models to create consistent KPI definitions and reusable analytical datasets for recurring financial and operational reporting. Created detailed Excel financial models supporting budgeting, forecasting, scenario planning, and what-if analysis across revenue, costs, investments, workforce planning, and profitability, contributing to approximately 25% improvement in forecasting accuracy. Designed and automated Excel and Power Query reporting workflows, consolidating data from multiple sources, standardizing calculations, and reducing repetitive manual preparation while improving consistency and reducing reporting errors. Built and optimized ETL and data-transformation workflows using Microsoft Fabric and Power Query, integrating multi-source datasets and implementing cleansing, validation, and transformation rules that improved data quality and reporting efficiency by approximately 20%. Used Python and R for exploratory and statistical analysis, investigating performance trends and root causes behind unexpected business results and providing quantitative insights that supported management recommendations. Worked directly with Finance, FP&A, operations, and business stakeholders to gather requirements, define KPIs, validate analytical results, and translate business questions into scalable dashboards, reports, and data solutions. Maintained analytical scripts, SQL queries, reporting logic, and dashboard-related code in Git, supporting version control, reproducibility, documentation, and controlled updates across reporting projects. Environment: SQL, Python, R, Power BI, Tableau, DAX, Microsoft Fabric, Power Query, Advanced Excel, Excel VBA, Git, ETL/ELT, Data Transformation, Data Cleaning, Data Validation, Data Modeling, Star Schema, Semantic Models, Financial Modeling, Budgeting, Forecasting, Variance Analysis, Scenario Analysis, What-If Analysis, KPI Reporting, Statistical Analysis, Root-Cause Analysis, Financial & Operational Analytics, Agile/Scrum. Goldman Sachs Junior Data Analyst (2019 2020) | Remote Supported financial and risk analytics for Global Markets teams, analyzing large datasets covering equities, fixed income, derivatives, P&L, exposure, and capital metrics to provide timely insights for business and risk decisions. Used SQL to extract, join, aggregate, and validate financial datasets, building reusable queries to analyze risk exposure and identify volatility-driven P&L impacts, improving identification of significant market movements by approximately 15%. Performed VaR, stress-testing, limit-utilization, and risk-exposure analysis, translating complex risk metrics into practical insights that helped Global Markets leadership monitor portfolio performance and escalate emerging risks. Developed interactive Power BI and Tableau dashboards for risk, finance, and management reporting, presenting KPIs, capital metrics, exposure trends, stress impacts, and financial performance in an easy-to-understand format. Supported Basel III and CCAR regulatory reporting by validating RWA, capital ratio, and stress-testing inputs, performing source-to-report reconciliation and data-quality checks that reduced manual regulatory review effort by approximately 20%. Prepared and analyzed monthly MIS reports covering financial performance, operating metrics, cost trends, risk indicators, and management KPIs, providing senior stakeholders with clear insights for business-performance reviews. Performed budgeting, forecasting, and variance analysis, comparing actual performance against budget and prior periods to identify cost drivers, unusual movements, and opportunities for improved financial planning. Automated recurring finance and risk reporting using Advanced Excel, Power BI, SQL, and standardized reconciliation processes, improving reporting accuracy by approximately 25% and reducing repetitive manual work across reporting cycles. Conducted detailed data validation, reconciliation, and trend analysis across finance and risk datasets, investigating discrepancies and implementing standardized quality checks to improve consistency and maintain audit-ready reporting. Worked closely with Finance, Risk, Operations, Technology, and Global Markets stakeholders to gather reporting requirements, explain analytical findings, prepare executive and regulator-facing summaries, and deliver accurate financial insights within tight reporting deadlines. Environment: SQL, Advanced Excel, Power BI, Tableau, R, Financial Data Analysis, Risk Analytics, P&L Analysis, VaR, Stress Testing, Limit Utilization, Basel III, CCAR, RWA, Capital Ratio Analysis, Financial Reporting, MIS Reporting, Budgeting, Forecasting, Variance Analysis, Data Validation, Data Reconciliation, Data Quality, KPI Reporting, Trend Analysis, Regulatory Reporting, Audit Readiness, Executive Reporting. Education Master of Science in Data Analytics - Texas State University, Round Rock, Texas | 2017 - 2018 Bachelor of Science in Finance - University of Texas at Dallas, Dallas, Texas | 2013 - 2017 Keywords: quality analyst business intelligence sthree rlang information technology trade national Tennessee Texas