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Design, build, and maintain scalable batch and streaming ETL/ELT pipelines within cloud environments like Databricks and Snowflake. Collaborate with cross-functional teams to prepare reliable datasets for knowledge graphs, AI workflows, and machine learning platforms.
This is a remote position.
We are looking for two Data Engineers to build and maintain the scalable pipelines and lakehouse infrastructure that power our analytics, machine learning, and AI agent platforms. You will ensure that clean, reliable, and well-governed data flows seamlessly from raw operational sources into analytical layers, knowledge graphs, and AI workflows.
Key Responsibilities:
Design, build, and maintain scalable batch and streaming ETL/ELT pipelines in cloud environments (Databricks / Snowflake / AWS / Azure).
Transform raw structured and semi-structured data into clean, curated analytical models (medallion architecture: Bronze/Silver/Gold) using SQL, Python, and dbt.
Implement automated data quality checks, schema validation, logging, and pipeline observability.
Collaborate closely with Ontology, Knowledge Graph, and Data Science teams to prepare reliable datasets for downstream graph ingestion and ML feature stores.
Enforce data governance, lineage, and security standards (role-based access control, PII handling, GDPR compliance).
3+ years of hands-on experience as a Data Engineer building production data pipelines.
Strong proficiency in SQL and Python, with practical experience in distributed data processing (Databricks and PySpark).
Hands-on experience with modern data platforms (Databricks, Snowflake, AWS, or Azure) and transformation/orchestration tools (dbt, Apache Airflow, or Databricks Workflows).
Solid understanding of data modeling concepts (dimensional modeling, lakehouse architecture) and CI/CD best practices (Git).
Detail-oriented approach to data reliability, performance tuning, and pipeline monitoring.
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