Data & Database Engineering
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).
10 leveled profiles. Pick a level to see the full profile.
Individual contributor
Database Engineering — designs, implements, tunes, secures, and operates relational and NoSQL database systems (MySQL, PostgreSQL, Oracle, SQL Server, MongoDB, Cassandra, DynamoDB) and managed cloud database platforms (Azure SQL, Cosmos DB, Snowflake, Redshift). Distinct from Data Pipeline/ETL Engineering (which centers on data movement and transformation workflows) and Data Modeling/Analytics focuses: this focus owns the database engine itself — performance tuning, backup/recovery, security configuration, schema design, clustering, and reliability of the persistence layer.
Database Engineering — designs, implements, tunes, secures, and operates relational and NoSQL database systems (MySQL, PostgreSQL, Oracle, SQL Server, MongoDB, Cassandra, DynamoDB) and managed cloud database platforms (Azure SQL, Cosmos DB, Snowflake, Redshift). Distinct from Data Pipeline/ETL Engineering (which centers on data movement and transformation workflows) and Data Modeling/Analytics focuses: this focus owns the database engine itself — performance tuning, backup/recovery, security configuration, schema design, clustering, and reliability of the persistence layer.
Database Engineering — designs, implements, tunes, secures, and operates relational and NoSQL database systems (MySQL, PostgreSQL, Oracle, SQL Server, MongoDB, Cassandra, DynamoDB) and managed cloud database platforms (Azure SQL, Cosmos DB, Snowflake, Redshift). Distinct from Data Pipeline/ETL Engineering (which centers on data movement and transformation workflows) and Data Modeling/Analytics focuses: this focus owns the database engine itself — performance tuning, backup/recovery, security configuration, schema design, clustering, and reliability of the persistence layer.
Database Engineering — designs, implements, tunes, secures, and operates relational and NoSQL database systems (MySQL, PostgreSQL, Oracle, SQL Server, MongoDB, Cassandra, DynamoDB) and managed cloud database platforms (Azure SQL, Cosmos DB, Snowflake, Redshift). Distinct from Data Pipeline/ETL Engineering (which centers on data movement and transformation workflows) and Data Modeling/Analytics focuses: this focus owns the database engine itself — performance tuning, backup/recovery, security configuration, schema design, clustering, and reliability of the persistence layer.
Database Engineering — designs, implements, tunes, secures, and operates relational and NoSQL database systems (MySQL, PostgreSQL, Oracle, SQL Server, MongoDB, Cassandra, DynamoDB) and managed cloud database platforms (Azure SQL, Cosmos DB, Snowflake, Redshift). Distinct from Data Pipeline/ETL Engineering (which centers on data movement and transformation workflows) and Data Modeling/Analytics focuses: this focus owns the database engine itself — performance tuning, backup/recovery, security configuration, schema design, clustering, and reliability of the persistence layer.
Management
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).
Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).