We are seeking a **Senior level Data Engineer** to lead the design and implementation of a modern AWS lakehouse for a data rich enterprise. **Requirements:** * **Minimum \+6 years** building and operating large scale data platforms and ETL pipelines in production. * **Strong hands on experience** with **AWS** data services including **S3 Glue EMR DMS MWAA Athena and Redshift.** * Deep working knowledge of **Spark** development in **PySpark** or **Scala** and migration of **Hive HQL to Spark.** * Practical experience with open table formats such as **Apache Iceberg** or **Delta Lake** and registering tables in **Glue Catalog.** * Proven experience designing and operating **Airflow DAGs** and integrating Airflow with AWS services. * Strong **SQL** skills and production experience with dbt or equivalent SQL modeling frameworks. * Experience implementing data quality frameworks and profiling, using Deequ Great Expectations or Glue Data Quality. * Familiarity with data lineage and observability practices, and experience with OpenLineage or equivalent integrations. * **Solid understanding of security and compliance** for consumer data including IAM KMS Lake Formation and approaches for audit and deletion requests. * Excellent verbal and written communication skills, including stakeholder interviews and executive level presentations. **We Offer:** * 100% remote job. * Long term job. * Stability. * Carreer path. * English lessons. * Other benefits. Job Type: Full\-time Pay: From $5,000,000\.00 per month Application Question(s): * Please share your gross salary expectation in COP Experience: * AWS: 4 years (Required) * PySpark: 4 years (Required) * dbt: 3 years (Required) * Airflow: 3 years (Required) * hive: 1 year (Preferred) Language: * English (Required) Work Location: Remote