Open to opportunities

Jordan Tran

@jordantran

Message

Senior Data Engineer building petabyte-scale data platforms and ML pipelines.

United States

Message

What I'm looking for

I’m looking for a role where I can own reliable batch+streaming pipelines, improve data quality and observability, and design lakehouse architectures that scale—so teams can move faster with trustworthy analytics and ML-ready data.

I’m a Senior Data Engineer with 11+ years of experience building large-scale data platforms and ML data pipelines at Amazon, Databricks, and Spotify. I specialize in event ingestion, lakehouse architecture, distributed data processing, and production-grade data systems running at petabyte scale.

At Amazon, I led development of batch and streaming data pipelines supporting OneMedical, implementing Kafka + Databricks real-time ingestion to improve event freshness and downstream decision-making. I’ve also strengthened reliability, observability, and recovery to maintain 99.9%+ uptime while improving data quality and auditability, and I optimized Spark performance through partitioning strategies and storage layout changes. Earlier, at Databricks, I helped scale Delta Lake with transactional reliability and schema enforcement, operationalized MLflow-based experiment and handoff workflows, and replaced fragile Parquet ETL patterns with auditable, production-ready pipelines.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Amazon

Aug 2020 - Present (5 years 8 months)

Built large-scale batch and streaming data pipelines using Kafka and Databricks to improve event freshness for healthcare analytics and decision-making. Improved reliability and observability to maintain 99.9%+ uptime while strengthening data quality, auditability, and Spark performance.

Kafka Apache Spark Delta Lake Amazon S3 Amazon Redshift Amazon EMR Data Pipelines Data Auditing

Data Engineer

Databricks

Aug 2018 - Jul 2020 (1 year 11 months)

Contributed to foundational lakehouse initiatives, scaling Delta Lake with transactional reliability, schema enforcement, and auditability for concurrent batch and streaming workloads. Operationalized MLflow workflows for experiment tracking and reproducible handoffs, improving performance and operability of large-scale Spark pipelines.

Delta Lake Apache Spark MLFlow Parquet Apache Hive Avro ETL Reliability & Auditability

Backend Engineer

Spotify

Jul 2015 - May 2018 (2 years 10 months)

Worked on Spotify’s Event Delivery Infrastructure migration and Discover Weekly personalization pipelines during rapid user growth, supporting cloud migration and event reliability. Helped scale event processing to 100B+ daily events across 500+ event types and improved analytics job efficiency with Parquet migration and query optimization.