Data Infrastructure Engineer — Build Clean, Trusted Datasets

Economic Intelligence Co.

San Francisco, Northern (CA, KY)

Hybrid

USD 140,000 - 210,000

Full time

14 days+

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Job summary

Economic Intelligence Co. in San Francisco (SF preferred) is seeking a Software Engineer, Data Infrastructure to build the data backbone for our models, evals, and incubation teams.

You will own batch and streaming pipelines, storage and lineage, and tools for researchers to query and trust the data.

A strong background in Python and SQL with Spark, Kafka, Airflow and dbt will help turn messy traces into clean, reliable datasets.

Qualifications

  • Strong Python and SQL with experience in Spark, Ray, Kafka, Flink, Airflow, dbt or similar.
  • Strong instincts for data quality, schema design, performance and cost.
  • Built systems where correctness, lineage and reliability mattered.
  • Able to turn messy events and traces into clean datasets without losing context.
  • Comfortable working across infra, research, product, and operations.
  • Notices when something is off before anyone else does.

Responsibilities

  • 01 Batch and streaming pipelines for training, post-training, evaluation, and product data.
  • 02 Storage, processing, lineage, deduplication, quality checks, and dataset versioning for company-building traces.
  • 03 Data systems that connect agent activity to outcomes: demand, customers, delivery, revenue, and verification.
  • 04 Tools researchers and engineers use to query, inspect, debug, and trust the data.

Skills

Python
SQL
Spark
Kafka
Flink
Airflow
dbt
Data quality
Schema design
Reliability

Tools

Spark
Kafka
Flink
Airflow
dbt
Ray

Job description

Economic Intelligence Co. in San Francisco (SF preferred) is seeking a Software Engineer, Data Infrastructure to build the data backbone for our models, evals, and incubation teams.

You will own batch and streaming pipelines, storage and lineage, and tools for researchers to query and trust the data.

A strong background in Python and SQL with Spark, Kafka, Airflow and dbt will help turn messy traces into clean, reliable datasets.

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