Data engineering services — senior engineers, time & material

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Senior data engineers from Eastern Europe, rented on a time & material basis. We are the employer of record. No fixed project scope. Engineers on your sprint in about 2 weeks.


What these engineers build

Data pipelines — ingestion from APIs, databases, event streams, and flat files into a central store. Batch (Airflow, Prefect) and real-time (Kafka, Flink).

Data warehouses — Snowflake, BigQuery, Redshift, Databricks. Modeling with dbt. Schema design, performance tuning, incremental loads.

Data lakes and lakehouses — raw storage (S3, ADLS), Spark processing, Delta Lake / Apache Iceberg for ACID transactions on the lake.

ML feature stores — data pipelines feeding feature stores (Feast, Tecton) that serve ML models in production.

ELT architecture — Fivetran/Airbyte for ingestion, dbt for transformation, orchestration with Airflow or Dagster.


Stack coverage

LayerTools
OrchestrationAirflow, Prefect, Dagster
Transformationdbt, Spark, PySpark
WarehouseSnowflake, BigQuery, Redshift, Databricks
IngestionFivetran, Airbyte, Kafka, Kinesis
StorageS3, ADLS, GCS, Delta Lake, Iceberg
LanguagePython, SQL
CloudAWS, Azure, GCP

Roles available


Capabilities


Why Eastern Europe for data engineering

Romania in particular has deep supply in data engineering — a legacy of enterprise systems work (SAP, legacy modernization) that built strong SQL and pipeline fundamentals before the modern data stack existed. The senior cohort has 10–15 years of commercial experience.

50–70% below US senior rates. Inside the EU legal perimeter. CET/EET timezone.

Eastern Europe: the full breakdown


How an engagement starts

  1. Fill in the Estimator — use case, data readiness, timeline
  2. We return a recommended team composition and monthly cost band
  3. Engineers introduced within 5 business days
  4. On your sprint within 2 weeks

We handle local payroll and compliance.


Open the AI Engagement Estimator →