Senior Data Engineer

About the job

What You Will Do Design and build scalable ETL/ELT data pipelines using orchestration tools such as Apache Airflow, Prefect, or Dagster to ingest, transform, and deliver data across enterprise systems. Architect and maintain cloud data warehouses and lakehouses on platforms such as Snowflake, Databricks, Google BigQuery, or Amazon Redshift. Develop modular, tested data transformation layers using dbt or equivalent frameworks, enforcing version control, documentation, and data-quality standards. Build real-time and near-real-time streaming pipelines using tools such as Apache Kafka, AWS Kinesis, or Azure Event Hubs for event-driven architectures. Implement data governance, lineage tracking, and quality monitoring using tools such as Monte Carlo, Great Expectations, or Atlan. Optimize query performance, storage costs, and compute utilization using partitioning, clustering, materialization strategies, and FinOps principles. Collaborate with international analytics teams, data scientists, and business stakeholders to translate requirements into production-grade data models. What We’re Looking For 5+ years of data engineering experience building production data pipelines and warehouse architectures. Expert-level proficiency with at least one cloud data platform (Snowflake, Databricks, BigQuery, or Redshift) and SQL-based transformation frameworks like dbt. Hands-on experience with workflow orchestration tools (Apache Airflow, Prefect, or Dagster) and CI/CD for data pipelines. Strong programming skills in Python or Scala for data processing, API integrations, and custom transformations. Experience with data modeling methodologies (dimensional modeling, Data Vault, or equivalent) and data quality/governance tooling. Flawless English communication skills to present pipeline architectures to data and engineering leadership.