Data Engineering is one of the most interesting and broad topics itself, and we’re not limited to any particular topic. We are not restricted in technologies, languages, and platforms, use this list as an example of what might be interesting for us!

Data computational Models
Data Quality
Data Standards
Data Modelling
Data Visualization
Data Lake and Data Catalog
Low-code development
Business analytics
Business intelligence
Data governance: Availability, Usability, Integrity, Security, Migration
Data Infrastructure: Logging and Tracing, Cloud services, Private cloud, BigData as a service, Data-intensive applications, Data ops (Orchestration and Tooling), ML Ops
Data Science: Analytics, Change(Anomaly) detection, 3D Vision, Deep learning
Technologies: KubeFlow, MLFlow, K8S, Yarn, SGE, LSF, PBS/Torque, Ignite, Hive, Impala, Presto, Vertica, ClickHouse, Cassandra, Teradata, Redshift, GreenPlum, Exadata, MSSQL, PostgreSQL, MongoDB, DynamoDB, S3, ADLS, GCS, HDFS, Spark, Flink, Hadoop, and other MapReduce existent and non existent frameworks