Jordan Zaman

Senior Data Engineer with over 10 years of experience in the software industries, specializing in data architecture, pipeline development, and large-scale data processing. Expertise in designing and optimizing scalable, high-performance data solutions, leveraging technologies such as Apache Spark, Hadoop, Kafka, AWS, Azure, GCP, Snowflake, DBT, and PostgreSQL. Proficient in ETL/ELT workflows, real-time and batch data processing, and cloud-native data engineering.

Skilled in leading cross-functional teams, collaborating with stakeholders, and driving data-driven decision-making by developing efficient, secure, and reliable data infrastructures. Passionate about building scalable data ecosystems to support business intelligence, AI/ML models, and enterprise analytics.

Senior Data Engineer - Accenture - New York City, NY

(2022-06)

• Engineered and managed highly scalable data platforms by integrating Apache Spark, Databricks, and Kafka with AWS and GCP services, enhancing operational intelligence across multiple business functions.
• Spearheaded the design and deployment of Databricks Lakehouse architectures, optimizing Delta Lake and Unity Catalog for data reliability, efficient schema evolution, and reduced latency in delivering real-time analytics.
• Developed streaming pipelines using Spark Structured Streaming and Kafka, powering interactive dashboards and automating event-driven systems to facilitate real-time decision-making and operational agility.
• Led the implementation of Airflow-based orchestration with robust features like SLA enforcement, retries, backfills, and alerting, ensuring mission-critical data workflows were executed reliably across environments.
• Played a key role in multi-cloud initiatives across AWS and Azure, overseeing seamless cloud migrations and optimizing hybrid ingestion strategies, enabling flexible, cross-cloud access for diverse datasets.
• Built and optimized ETL/ELT pipelines in Python and SQL, automating the ingestion of structured, semi-structured, and unstructured data from multiple sources (APIs, relational databases, flat files, streams).
• Delivered curated analytics-ready datasets to fuel BI reporting, enhance operational KPIs, and enable advanced machine learning and AI workloads, supporting data-driven decision-making.
• Collaborated with product engineering teams to expose high-value data through REST and gRPC APIs, enabling secure, schema-driven integrations to meet cross-functional needs.
• Established and enforced data quality frameworks, proactively identifying pipeline issues and leveraging anomaly detection to ensure data integrity across the workflow.
• Integrated enterprise-grade security controls, including RBAC, encryption, and PII masking/tokenization, to ensure regulatory compliance and protect sensitive data assets.
• Pioneered the development of CI/CD pipelines using GitHub Actions and Azure DevOps, automating the deployment and validation of data infrastructure changes, enhancing the speed and reliability of updates.
• Optimized Spark performance and SQL queries, focusing on improving resource efficiency and minimizing computational costs, while meeting high-throughput, low-latency processing requirements.
• Supported AI/ML teams by preparing feature-rich datasets, providing key metrics for experiments, and ensuring smooth integration with production-level data feeds for continuous model training.
• Documented system architecture, data lineage, and operational processes to ensure transparency, reliability, and audit compliance across the data platform.
• Led technical initiatives by reviewing designs, conducting code reviews, and driving best practices in collaboration with cross-functional data teams to maintain high standards in development and operational efficiency.

Data Engineer - Salesforce - San Francisco, CA

(2018-03 - 2022-05)

• Led the design and automation of ETL workflows using Apache Airflow and AWS CloudWatch, significantly improving the reliability, performance, and scalability of data pipelines. This reduced manual intervention and streamlined data processing workflows.
• Integrated real-time data feeds from multiple sources into Tableau and other BI platforms, ensuring that reports and dashboards were available with up-to-date information for immediate business decisions. This reduced report generation time by 30%.
• Collaborated with the cloud engineering team to migrate traditional data processing workflows to cloud-based solutions, improving data processing time and scalability across AWS Redshift and Amazon S3 platforms.
• Built predictive models using Scikit-learn to support customer segmentation and churn prediction efforts, leading to more targeted marketing campaigns and improved customer retention strategies.
• Implemented automation scripts using Python to streamline the repetitive tasks of data collection, transformation, and reporting, saving over 20 hours per week across the engineering team.
• Created and optimized SQL queries to improve database performance for large-scale reporting, resulting in more efficient data retrieval and faster decision-making.
• Led the integration of third-party APIs and external data sources, enabling seamless data onboarding and processing for clients, improving business insights and reducing the time to insight.
• Optimized data pipelines for better efficiency and faster processing by improving the architecture of SQL-based data models, implementing indexing strategies, and improving schema design to enhance query performance.
• Established real-time monitoring dashboards using AWS CloudWatch and Logstash, allowing teams to quickly identify and resolve bottlenecks in ETL workflows.
• Worked closely with cross-functional teams, including data scientists, analysts, and business stakeholders, to ensure accurate, timely data delivery and alignment on project goals.
• Delivered high-impact insights by building data visualizations and dashboards in Tableau, enabling senior leaders to track KPIs and make data-driven decisions across various departments.

Junior Data Developer - Dropbox - San Francisco, CA

(2016-05 - 2018-02)

• Designed and maintained ETL/ELT pipelines that processed large-scale datasets from multiple sources, ensuring the delivery of clean, accurate data for reporting and analysis.
• Automated key data analysis and processing tasks using Python and SQL, resulting in a 40% increase in team efficiency by reducing manual interventions and allowing the team to focus on higher-value analysis.
• Developed and deployed regression models in R and Python to predict credit risk and improve financial decision-making processes, enhancing risk assessment models used in the loan underwriting process.
• Built and maintained interactive dashboards and data visualizations in Tableau and QlikView to track key business metrics, helping non-technical stakeholders quickly interpret data and make informed decisions.
• Enhanced SQL query performance by optimizing database schema designs, improving report generation times by 50%, and facilitating better data access for end-users.
• Led the integration of third-party data sources and external APIs to provide additional context for financial risk analysis, improving decision-making and reducing risk exposure.
• Worked with cross-functional teams to align on project goals and ensure that the data infrastructure was scalable and adaptable to future business requirements.
• Facilitated collaboration between data engineers and product managers to define and implement data integration solutions for new digital features, improving customer engagement and retention.
• Improved cloud infrastructure by migrating legacy systems to AWS, enhancing the scalability and flexibility of data processing workflows.
• Ensured consistent data quality and governance by developing standardized data pipelines and enforcing data management best practices.
• Trained junior engineers on data transformation techniques and automation frameworks, improving team performance and fostering a culture of continuous learning and development.

Bachelor of Science (BS) - Computer Science - Stanford University (2011 - 2015)