26 paquetes
4 industrias publican esta familia.
Install-ready data-engineering-focused OpenClaw agents for industry-1-software-it.
4 industrias publican esta familia.
Builds production data pipelines and platform components with Python and SQL, focusing on reliability, maintainability, and usable data delivery.
Builds agency data pipelines and warehouse workflows with Python and SQL so campaign, ecommerce, and content datasets stay reliable and analysis-ready.
Builds offline data platform pipelines with Scala so marketplace analytics datasets remain reliable, scalable, and usable across downstream teams.
Designs agency data models with SQL so analytical entities, attribution datasets, and reporting outputs stay consistent and easier to maintain.
Builds marketplace data observability and lineage capabilities so datasets stay traceable, diagnosable, and easier to trust across analytics and platform workflows.
Builds data quality and lineage capabilities so marketplace datasets stay trustworthy, explainable, and easier to govern across the platform.
Builds data quality and lineage capabilities so platform datasets remain trustworthy, traceable, and easier to govern across analytics workflows.
Builds agency data warehouse structures with SQL so business entities, campaign metrics, and reporting layers remain scalable and analyzable.
Designs marketplace data warehouse models so core business entities, metrics, and reporting structures stay consistent and analyzable.
Designs Scala-based warehouse models so content, creator, and growth metrics remain structured, analyzable, and consistent across teams.
Builds ETL and ELT workflows so marketplace data ingestion and transformation pipelines remain dependable, observable, and efficient.
Builds ETL and ELT workflows with Python and SQL, improving ingestion quality, transformation reliability, and operational transparency across the data stack.
Builds ETL workflows with Python so source extraction, transformation logic, and delivery schedules remain dependable across agency data systems.
Builds Python ETL pipelines so content and product data can move through the platform with stronger reliability, observability, and downstream usability.
Builds instrumentation governance workflows so event tracking standards, data quality, and analytics trust remain consistent across product surfaces.
Leads the data platform function, aligning data infrastructure, delivery standards, and operating priorities with broader product and business needs.
Builds lakehouse platform capabilities with Java and Scala, improving scalable storage, unified analytics workflows, and trustworthy data foundations.
Builds metadata and lineage capabilities with Java and Python so data assets remain discoverable, explainable, and governed across the platform.
Builds Java real-time computing systems so streaming content, user, and platform events can be processed reliably at production scale.
Builds real-time data processing systems with Java and Scala, emphasizing throughput, correctness, and resilient streaming operations.
Builds real-time marketplace data systems with Java so streaming applications can process events reliably at production scale.
Builds real-time feature and profile pipelines so recommendation, growth, and safety systems receive fresher behavioral signals with dependable latency.
Builds real-time metrics systems so marketplace teams can monitor operational signals and decision dashboards with fresher data.
Builds real-time risk data pipelines so marketplace fraud and governance systems receive timely, reliable decision signals.
Builds Scala stream processing jobs so high-volume platform events can flow through reliable, maintainable, and timely real-time pipelines.
Builds streaming computing pipelines so marketplace event processing remains timely, resilient, and operationally manageable.