Most data engineering experience sections read like a job spec: “built pipelines,” “maintained the warehouse,” “worked with stakeholders.” A hiring manager skimming 40 profiles can't tell an ETL maintainer from a platform builder off bullets like that — and LinkedIn search can't either.
A strong data bullet names the stack, the scale and one reliability or cost outcome. The 6 rewrites below cover batch, streaming, platform and analytics-engineering work — steal the structure, then score your own.
Spark, Airflow, Kafka, dbt, Snowflake — the tools in the bullet are the terms in the recruiter's search.
Jobs per day, events per second, terabytes served: scale numbers are what separate operating from observing.
On-time delivery, cost cut, freshness gained — one result per line reads like an engineer, not a job description.
Name the scheduler or the broker and the daily volume — then close with reliability or speed.
Built and maintained data pipelines for the company.
Own 400+ daily Airflow pipelines (Python, Spark) feeding the company Snowflake warehouse, lifting on-time delivery from 92% to 99.9% with data-quality gates.
Worked on real-time data processing.
Built Kafka + Flink streaming pipelines processing 2B events/day, replacing nightly batch loads with minute-fresh data for product analytics.
Migrated old ETL jobs to new systems.
Rewrote 200 legacy Informatica jobs as Airflow DAGs during a warehouse migration, cutting the nightly load window from 6 hours to 45 minutes.
Platform bullets verify self-serve scale; analytics bullets verify modeled, tested data.
Optimized Snowflake usage to save money.
Re-partitioned the 50 largest Snowflake tables and right-sized warehouses, cutting compute spend $480K/year while query latency fell 40%.
Created dbt models and dashboards.
Built and documented 120 tested dbt models on Snowflake powering Looker dashboards for 600 users, with one audited source of truth for company KPIs.
Led data platform improvements for the team.
Led migration to a domain-owned lakehouse on Databricks (Delta Lake, Unity Catalog) for 8 data domains, taking freshness from daily to hourly and cutting cross-team breakage 70%.
Think your LinkedIn beats this score?
The strongest data engineer experience-bullet rewrite on this page scores 81/100 on the recruiter-search signals our free check measures. Post the challenge, or see what your own profile scores.
The strongest data engineer experience-bullet rewrite on this page scores 81/100 (strong visibility) for recruiter search. Think your LinkedIn beats it? Free 60-second check: https://standout13.acoco.ai/linkedin-experience-bullet-examples/data-engineer
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