Senior Data Engineer

Dynata is hiring a Senior Data Engineer to create and support the data flows, transformation layers, and models that support its enterprise lakehouse. The role focuses on building reliable ELT and ETL solutions, working on both batch and streaming processing, and handling issues such as failed pipelines and performance problems. It also involves integrating different kinds of data, improving physical and logical data models with data architects, setting up data quality checks, and taking part in code reviews and engineering standards. The advert names Apache Spark, Kafka, Flink, Delta Lake, Iceberg, SQL, PySpark, Great Expectations, dbt, Terraform, Helm, Airflow, Prefect, Dagster, AWS Glue, Azure Data Factory, GCP Dataflow, Git, CI/CD, Docker, Kubernetes, Databricks, Snowflake, and Apache Hudi. Applicants should have at least 6 years of data engineering experience and a bachelor’s degree in Computer Science, Engineering, or a related technical field. Strong Python, SQL, and PySpark skills are required, along with experience in enterprise-scale delivery. The role is remote and full-time. It is aimed at experienced data engineers who can take a technical lead and support less experienced colleagues through mentoring and knowledge sharing.