Steffi Christopher

As the Head of Data Engineering, Analytics & Operations at Pattern Energy, I orchestrate the strategic deployment of data and analytic solutions across diverse platforms, ensuring alignment with stringent data residency regulations. I drive transformative initiatives in Operations, Governance, and Literacy to instill trust in data, profoundly impacting insight-driven culture.

• Spearhead the data & AI strategy, engaging executive leadership to secure multi-year investments worth $10 million. This strategic direction fostered the development of a Single Source of Truth for enterprise metrics and robust AI capabilities, intricately intertwined with the overarching business strategy
• Designed and developed a real time pipeline using Kafka, Flink, and Iceberg to process 200TB of data per day for BI and ML teams
• Institutionalize a robust data governance framework, standardizing metric definitions, enhancing data quality, and fostering data literacy across the organization leading to an increase in 30% efficiency overall
• Fostered the adoption of a comprehensive data catalog tool, seamlessly integrating it into the workflows of IT, Data Engineering, and business users

• Established the Data Engineering org and managed 3 teams of 12 engineers, and engineering managers responsible for developing data products at Spekit to enable value-driven insights
• Formulated engineering processes including CI/CD, TDD framework, Data Observability, and Data Governance to achieve regulatory compliance per GDPR and CCPA
• Developed ETL pipelines and analytics data warehouse using dbt and customer-facing analytics platforms using Tableau and Explo
• Designed and implemented company-wide real-time event tracking system using Segment to gain insights into product usage and customer behavior to drive retention and minimize churn
• Built a summarizer to facilitate real-time learning using LLMs to reduce content generation time by 50%
• Hired and mentored high-caliber managers and engineers with diverse technical strengths and backgrounds across geographically dispersed teams

• Managed 3 teams of engineers, data scientists, and technical leads that developed and maintained data products supporting Geotechnical, Finance, and Accounting teams
• Built prediction models in Python using Logistic Regression, Random Forest, and Neural Networks to predict probability of wind generation for specific regions with varying wind conditions
• Built text mining frameworks using NLP from incident logs to analyze failure conditions for wind turbines
• Improved forecasting methods to predict net megawatts generated across all wind farms by 10% using SVM best tuning and Quantile Regression Forest
• Developed real-time data processing solutions to process extensive volume data from SCADA systems in globally located wind turbines to power analytics dashboard and provide executive management with metrics like MWh production and revenue generation using Kafka, KStreams, and HBase
• Supported Accounting and FP&A teams with financial data for their monthly and quarterly planning, close process, variance analysis, and reporting in Tableau and Power BI
• Built multiple ETL jobs from various sources to power data warehouse in Snowflake using Airflow

• Designed and implemented a batch data pipeline to process IoT data using Azure Data Factory, resulting in a 50% reduction in processing time
• Developed a self-service dashboard in Power BI for the Geotechnical teams resulting in a 25% increase in data-driven decision making
• Studied and optimized market funding programs by creating a statistical model in Python using exponential curve and Monte Carlo Simulation to estimate probabilities of exceeding budgets

• Developed an analytics engine to process financial events using Java, Hadoop, and MapReduce
• Implemented new technologies using Power Automate and Power BI to automate financial reporting that saved around 5,000 hours annually for end users

• Automated ETL processes, making it easier to wrangle data and reducing latency between systems
• Formulated approaches for automation and optimization of data reporting workflows, resulting in 80% reduction in manual data input
• Created SQL scripts to extract and reshape data in alignment with business needs, leading to a remarkable 95% enhancement in accuracy
• Developed dashboards to provide crucial performance trends and insights using Tableau

• Created analytics dashboards in OBIEE by translating business requirements into technical solutions