Data Engineering Manager

A Data Engineering Manager is a data-focused technical software engineering manager that leads data engineering teams to build and maintain data infrastructure and data pipelines.

• AKA: Data Infrastructure Manager, Data Platform Manager, Analytics Engineering Manager, Data Team Manager.
• Context:
  • It can typically design Data Engineering Architecture including data engineering data lakes, data engineering data warehouses, and data engineering streaming systems.
  • It can typically build Data Engineering Pipelines for data engineering ingestion, data engineering transformation, and data engineering delivery.
  • It can typically ensure Data Engineering Quality through data engineering validation, data engineering monitoring, and data engineering governance.
  • It can typically enable Data Engineering Analytics by providing data engineering clean data, data engineering reliable access, and queries.
  • It can typically manage Data Engineering Scale handling data engineering petabytes, data engineering billion records, and data engineering real-time streams.
  • ...
  • It can often collaborate with Data Engineering Data Scientists on data engineering feature engineering and data engineering model data.
  • It can often partner with Data Engineering Analytics Teams on data engineering reporting needs and data engineering dashboard requirements.
  • It can often work with Data Engineering Product Managers on data engineering data products and data engineering data monetization.
  • It can often coordinate with Data Engineering Compliance Teams on data engineering privacy, data engineering GDPR, and data engineering data retention.
  • ...
  • It can range from being a Batch-Focused Data Engineering Manager to being a Streaming-Focused Data Engineering Manager, depending on its data engineering processing model.
  • It can range from being a Traditional Data Engineering Manager to being a Modern Data Engineering Manager, depending on its data engineering technology stack.
  • It can range from being a Centralized Data Engineering Manager to being a Decentralized Data Engineering Manager, depending on its data engineering organizational model.
  • It can range from being a Cost-Conscious Data Engineering Manager to being a Performance-Optimized Data Engineering Manager, depending on its data engineering optimization priority.
  • ...
  • It can implement Data Engineering Best Practices for data engineering data modeling, data engineering data lineage, and data engineering documentation.
  • It can establish Data Engineering SLAs for data engineering freshness, data engineering completeness, and data engineering accuracy.
  • It can create Data Engineering Self-Service capability for data engineering analysts and data engineering scientists.
  • ...
• Examples:
  • Data Engineering Platform Managers, such as:
    • Cloud Data Engineering Manager using data engineering BigQuery, data engineering Snowflake, or data engineering Databricks.
    • Open-Source Data Engineering Manager implementing data engineering Apache Spark, data engineering Apache Airflow, and data engineering Apache Kafka.
    • Hybrid Data Engineering Manager managing both data engineering on-premise and data engineering cloud infrastructure.
  • Data Engineering Domain Managers, such as:
    • Product Data Engineering Manager building data engineering product analytics, data engineering user behavior, and data engineering A/B testing pipelines.
    • Financial Data Engineering Manager handling data engineering transaction data, data engineering risk calculations, and data engineering regulatory reporting.
    • Marketing Data Engineering Manager processing data engineering campaign data, data engineering attribution, and data engineering customer journeys.
  • Data Engineering Specialty Managers, such as:
    • Real-Time Data Engineering Manager focusing on data engineering streaming architecture, data engineering event processing, and data engineering low latency.
    • ML Data Engineering Manager supporting data engineering feature stores, data engineering training pipelines, and data engineering model monitoring.
    • Data Governance Engineering Manager implementing data engineering catalogs, data engineering lineage tracking, and data engineering quality frameworks.
  • ...
• Counter-Examples:
  • Data Science Manager, which focuses on data analysis and machine learning rather than data infrastructure.
  • Analytics Manager, which emphasizes business insights rather than data pipelines.
  • Database Administrator Manager, which manages operational databases rather than analytical data systems.
• See: Software Engineering Manager, Data Engineering, Data Infrastructure, Data Pipeline, Backend Software Engineering Manager, Platform Engineering Manager, Data Science Manager, Business Intelligence Manager.

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