Pamela Austin

Enterprise Data Deduplication | AWS/Redshift Pipeline Development | Salesforce Integration

• Led end-to-end deduplication of 150M+ customer and transaction records by designing a business-criteria matching strategy and scalable AWS/Redshift SQL pipeline; orchestrated ETL with AWS Glue and automated CRM syncs to Salesforce using AWS AppFlow and Bulk API 2.0.
• Designed comprehensive deduplication strategy from business requirements: defined deterministic and fuzzy matching keys, heuristic scoring algorithms, authoritative source hierarchy (including Salesforce), and merge/survivorship rules supporting CRM, reporting, and analytics.
• Developed post-pipeline validation framework using Python (Pandas) to automatically query Redshift and Salesforce API, compare pre/post-merge record counts, and generate daily reconciliation reports—reducing manual QA and audit preparation time by 75%.
• Built production-ready deduplication pipeline in Amazon Redshift using advanced SQL (CTEs, window functions, staging tables) to score, identify, consolidate, and flag duplicates with full audit trail.
• Orchestrated ETL jobs with AWS Glue (PySpark), creating modular jobs for incremental processing, schema discovery, and automated retry logic.
• Implemented performance optimizations: tuned Redshift distribution/sort keys, compression encodings, partitioning strategies, and Glue job parallelism—reducing runtime and cluster costs.
• Produced comprehensive documentation: Confluence runbooks, technical specs, SQL snippets, Glue job descriptions, AppFlow mappings, and decision logs for seamless handoff.
• Partnered with Data Engineers on pipeline operationalization (CloudWatch monitoring, alerts, error handling) and Salesforce developers on Bulk API 2.0 integration.

Business Intelligence | Predictive Analytics | Process Automation | Data Integration

• Developed predictive ML model to forecast project completion timelines, enabling proactive decision-making, optimized resource allocation, and improved operational efficiency across marketing operations.
• Built custom Windows Form application that automated client onboarding and intake processes—reducing client setup time by 48% and improving data consistency across business development operations.
• Automated data ingestion pipelines using APIs and Fivetran/dbt, reducing manual data retrieval by 40% and enabling near-real-time analytics.
• Designed comprehensive data validation framework improving data integrity across Snowflake and Power BI; reduced dashboard refresh times by 25% for real-time stakeholder insights.
• Built interactive Power BI dashboards visualizing compensation trends, workforce costs, and budget allocations for executive-level financial planning and strategic decision-making.
• Integrated Workfront API with Snowflake for centralized marketing analysis, creating unified data models across disparate systems.
• Led Agile ceremonies: backlog management and sprint planning using story points and RICE method to prioritize high-impact deliverables.
• Partnered with Finance, HR, Marketing, and IT stakeholders, leading data discovery sessions and aligning analytics with organizational goals.
• Integrated and optimized data enrichment platforms (ZoomInfo, Salesforce SFDC) enhancing data accuracy and completeness for strategic decision-making.

Product Analytics | Data Integration | IBM Cloud Pak for Data | Financial Modeling

• IBM Cloud Pak for Data (CP4D) & Watson Query expert: Integrated diverse data sources, executed distributed queries, and optimized data workflows for real-time analytics—leveraging CP4D's data governance, AI-driven insights, and visualization tools.
• Analyzed software product performance by tracking user behavior, engagement metrics, and feature adoption; conducted A/B testing, user research, and competitor analysis to guide product enhancements.
• Designed KPI dashboards in Power BI tracking product success, adoption rates, and operational efficiency for executive stakeholders.
• Implemented automated data quality checks using Python and Talend, ensuring high-quality datasets for predictive modeling and reporting.
• Conducted campaign performance analysis measuring ROI, customer acquisition costs, and conversion rates across multiple marketing channels.
• Assisted in budgeting, forecasting, and financial modeling to optimize marketing spending and improve cost efficiency.
• Developed customized Workfront reports to analyze project data, track KPIs, and provide operational improvement insights.
• Utilized Snowflake and SQL to query, clean, and structure large datasets; built ETL pipelines for data transformation.
• Partnered with cross-functional teams (Product, Marketing, Finance, IT) on process optimization, automation, and analytics-driven decision-making.
• Participated in data governance initiatives ensuring data quality, security, and compliance across platforms.

• Delivered actionable insights for clients across finance, business intelligence, and healthcare through in-depth data analysis and research.
• Finance: Designed data collection methodologies improving accuracy and reliability; analyzed financial data to identify trends, optimize resource allocation, and support investment strategies.
• Business Intelligence: Developed interactive dashboards and reports using Power BI, Tableau, and Excel enabling clients to track KPIs and improve operational efficiency.
• Healthcare: Analyzed large healthcare datasets identifying patterns and quality improvement opportunities; developed predictive models forecasting healthcare outcomes.
• Performed statistical analysis including regression, hypothesis testing, and clustering to extract insights and improve business strategies.
• Conducted data validation and quality assurance ensuring accuracy, consistency, and integrity of datasets.

• Analyzed 100+ marketing campaigns providing audience insights that improved targeting strategies and increased ROAS by 20%.
• Applied statistical analysis and visualization tools (Tableau, Power BI) to measure campaign performance, tracking conversion rates, CTR, and CAC.
• Designed and executed A/B tests and multivariate experiments optimizing campaigns based on analytical results.
• Conducted consumer preference research, market trend analysis, and competitor benchmarking for data-driven product positioning.
• Developed Tableau dashboard visualizing sales and churn data; applied SQL and dbt to transform raw retailer data into analytical datasets.
• Performed data cleansing, transformation, and validation ensuring accuracy in campaign analytics and reporting.