Sr. Data Engineer

Responsibilities

• Design, build, and operate scalable, cloud‑native data platforms supporting both batch and streaming use cases.
• Ensure strong data governance, performance, and reliability within data ecosystems.
• Develop and operate data systems across major cloud platforms (AWS, Azure, GCP), designing scalable and cost‑efficient cloud‑native solutions.
• Build modern data architectures including data lakes, data lakehouses, and data hubs.
• Implement ingestion patterns and data collection pipelines using tools such as Kafka and AWS Glue.
• Work with modern storage formats including Apache Iceberg and Parquet.
• Design and develop real‑time streaming pipelines using Kafka, Flink, or similar streaming frameworks.
• Apply event‑driven architecture principles for low‑latency data processing.
• Transform and model data using SQL‑based frameworks and orchestration tools such as dbt, AWS Glue, and Airflow.
• Implement data modeling concepts such as Slowly Changing Dimensions (SCD) and schema evolution.
• Perform large‑scale data transformations (batch and streaming) using Apache Spark.

Qualifications

• Proven experience building and operating cloud‑native data solutions on AWS, Azure, and/or GCP.
• Strong understanding of modern data architectures, ingestion patterns, governance practices, observability, and platform best practices.
• Expertise in data ingestion tools (Kafka, AWS Glue) and modern storage formats (Iceberg, Parquet).
• Deep knowledge of real‑time data streaming and event‑driven architectures using Kafka, Flink, or similar frameworks.
• Advanced SQL modeling skills and experience with tools such as dbt, Glue, and Airflow.
• Strong knowledge of SCD techniques, schema evolution, and modern data modeling practices.
• Extensive hands‑on experience with Apache Spark for large‑scale transformations.