United States
Data Engineer with experience in designing, building, and optimizing cloud-native, big data, and lakehouse platforms across banking, healthcare, and analytics domains. Proven expertise in AWS, Azure, GCP, Snowflake, Spark, Airflow, Kafka, and dbt, delivering secure, scalable data pipelines and analytics-ready models. Strong background in DataOps, CI/CD, data governance, observability, and cost optimization, enabling high-quality, compliant, and business-ready data for stakeholders
Working as a Data Engineer on building a scalable cloud-based data platform. Designing and developing data pipelines for ingesting and transforming data. Creating data models and views to support business reporting and analytics. Exposing data through APIs for efficient and flexible consumption. Implementing data validation and quality checks to ensure accuracy. Collaborating with teams on architecture, security, and performance improvements. Focused on delivering reliable, scalable, and maintainable data solutions.
Designed and implemented AWS-native and lakehouse data pipelines using Glue, Lambda, S3, Snowflake, Delta Lake, and Apache Iceberg to process petabyte-scale financial datasets, improving throughput by 30%. Built analytics-ready data models (star and snowflake schemas) using Snowflake and Snowpark to support regulatory reporting, business intelligence, and downstream analytics. Developed and optimized Apache Airflow DAGs for batch and streaming ETL/ELT workflows, reducing execution time by 40% and improving pipeline reliability. Implemented data governance, lineage, and access controls using Collibra and AWS Lake Formation to ensure GDPR, SOX, and internal compliance requirements. Automated infrastructure provisioning and deployments using Terraform, Jenkins, and GitHub Actions, embedding DataOps best practices and reducing release cycles by 50%. Established data observability and quality monitoring with ELK Stack, Datadog, and Monte Carlo, proactively detecting issues before business impact. Applied FinOps and cost-optimization strategies across Redshift and Glue workloads, reducing monthly cloud spend by 20%.
Built and maintained real-time and batch ingestion pipelines using Apache Kafka, AWS Glue, and Redshift to process high-volume user activity data for 1M+ users. Integrated NoSQL and semi-structured data sources (MongoDB, JSON, APIs) via REST and GraphQL, reducing processing latency by 30%. Orchestrated complex workflows in Apache Airflow and implemented dbt transformations with automated testing, improving data reliability and analytics trust. Implemented data quality frameworks using Great Expectations, reducing downstream data issuesby 25%. Secured data pipelines with IAM, KMS, and encryption standards to meet HIPAA compliance. Containerized data workloads with Docker and implemented CI/CD pipelines for reproducible and scalable deployments. Partnered closely with analysts and product teams to deliver data products and dashboards that improved customer engagement and retention
Developed Azure-native ETL pipelines using Azure Data Factory, Databricks (Delta Lake, Hudi), and Synapse, reducing ETL execution time by 30%. Implemented Spark and Scala-based batch processing workflows to handle large-scale structured and unstructured datasets. Designed and optimized data warehouse schemas in Snowflake and Synapse to support analytics and BI use cases. Built modular dbt models with version-controlled SQL transformations and automated tests for transparency and maintainability. Delivered Power BI dashboards for executive leadership, enabling data-driven decision-making on financial KPIs. Automated deployments and environment management using Azure DevOps and Kubernetes, embedding audit and compliance controls.