Remote Senior data engineering role where reliability is part of the job: validation gates, idempotent backfills, monitoring, and shared pipeline frameworks on Databricks, Snowflake, Airflow, and dbt on AWS. I want to own a real slice of the platform, use AI inside a verified workflow, and join a team that values code review, documentation, and mentoring.

Daniel Hunt
@danielhunt
Senior Data Engineer, 10 years at Amgen, Realtor.com, and Amazon Prime Video. Reliable pipelines on AWS with Databricks, Snowflake, Airflow, and dbt.
What I'm looking for
At Amgen, I designed a Unity Catalog pipeline framework that lifted job success from 95% to 99.2% across 45 Databricks jobs. I also reduced Databricks spend by right-sizing clusters and moving nine small jobs to shared serverless compute.
At Realtor.com, I built Airflow and dbt pipelines on Snowflake that raised on-time delivery from 88% to 98% for marketing segments. I added anomaly checks that cut data incidents from seven to two per quarter.
Earlier at Amgen, I led a Delta Lake migration that accelerated nightly refreshes from six to three and a half hours. I also replaced Informatica mappings with incremental PySpark pipelines on Databricks.
At Amazon, I rebuilt hourly Hive jobs as Spark sessionization on Amazon EMR, providing quality metrics from roughly two billion daily events across about 30 device types. I also implemented a Java Kinesis consumer with DynamoDB and CloudWatch alarms for Thursday Night Football.
Experience
Work history, roles, and key accomplishments
Designed a Unity Catalog pipeline framework lifting job success from 95% to 99.2% across 45 Databricks jobs. Reduced dashboard p95 latency from 6s to 1.5s by modeling a Kimball star schema on Databricks SQL.
Built Airflow and dbt pipelines on Snowflake raising on-time delivery from 88% to 98% for marketing segments. Cut data incidents from 7 to 2 per quarter by implementing anomaly detection and idempotent backfill DAGs.
Rebuilt hourly Hive jobs as Spark (Scala) sessionization on Amazon EMR, providing quality metrics from ~2B daily events. Implemented a Java Kinesis consumer with DynamoDB and CloudWatch alarms for live-event alerting.
Education
Degrees, certifications, and relevant coursework
Amazon Web Services (AWS)
AWS Certified Big Data - Specialty, Big Data
2019 - 2019
AWS Certified Big Data - Specialty certification obtained in 2019.
Amazon Web Services (AWS)
AWS Certified Solutions Architect - Associate, Solutions Architecture
2018 - 2018
AWS Certified Solutions Architect - Associate certification obtained in 2018.
California State University, Northridge
Master of Science, Computer Science
2016 - 2018
Master of Science in Computer Science from California State University, Northridge, completed in 2018.
Amazon Web Services (AWS)
AWS Certified Developer - Associate, Development
2017 - 2017
AWS Certified Developer - Associate certification obtained in 2017.
California State University, Northridge
Bachelor of Science, Computer Science
2013 - 2016
Bachelor of Science in Computer Science from California State University, Northridge, completed in 2016.
Tech stack
Software and tools used professionally
Amazon Redshift
Snowflake
Apache Spark
AWS Glue
Tableau
Amazon Quicksight
Amazon CloudWatch
Kubernetes
Amazon EKS
Jenkins
GitHub Actions
dbt
PostgreSQL
Oracle
InfluxDB
Node.js
Databricks
Adobe Analytics
Slack
Terraform
Python
Java
Scala
Kafka
Grafana
Amazon Kinesis
Docker
Airflow
Amazon EMR
Amazon Athena
SQL
Delta Lake
Cursor
GitHub Copilot
Collibra
LangGraph
Prophecy
Unity Catalog
Power BI
AWS
Availability
Location
Authorized to work in
Salary expectations
Social media
Job categories
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