Lead the design, implementation and automation of Data Pipelines, sourcing data from internal and external systems, transforming the data for the optimal needs of various systems and business requirements.

More Uses of the Data Pipelines Toolkit:

  • Create critical infrastructure and best practices as you scale your computer vision team and maintain Data Pipelines critical to testing and training your algorithms.
  • Develop infrastructure for Data Pipelines, ETL and database systems, analytic tools, and signal processing software to support the data analytics team.
  • Ensure you possess a diverse skill set covering most of full stack development, user interfaces, data analysis and visualization, Data Pipelines, and relational databases.
  • Develop end to end automated Data Pipelines to support integrated analytics products spanning recruiting, workforce management, employee sensing, compensation and other areas.
  • Develop and maintain ETL Data Pipelines, integrating a wide range of data sources to support business applications and internal analytics needs.
  • Establish: design, build, manage and optimize Data Pipelines for data structures encompassing data transformation, data models, schemas, Metadata, data quality, and workload management.
  • Identify: monitor and maintain existing Data Pipelines by debugging data issues, releasing hot fixes and optimizing performance to ensure data quality and adherence to SLAs.
  • Develop proofs of concept and evaluate design options to deliver ingestion, search, Metadata cataloging and scheduling of Data Pipelines.
  • Organize: provision tools access, document processes, develop training guides, maintain/update Data Pipelines to support processes key performance indicators.
  • Use proven methods to solve business problems using Azure Data and Analytics services in combination with building Data Pipelines, data streams and system integration.
  • Ensure you champion; build new Data Pipelines, identify existing data gaps and provide automated solutions to deliver analytical capabilities and enriched data to applications.
  • Bring fresh ideas from areas like information retrieval, Data Pipelines, data storage, visualization, artificial intelligence, and natural language processing.
  • Ensure you champion; build distributed, scalable, and reliable Data Pipelines that ingest and process data at scale and in real time to feed machine learning algorithms.
  • Be accountable for creating and maintaining automated Data Pipelines, data standards, and best practices to maintain integrity and security of the data; ensure adherence to developed standards.
  • Be accountable for developing Data Pipelines/ETL feeds/applications as reading data from external sources, merge data, perform data enrichment and load in to target data destinations.
  • Systematize: design, implement and automate Data Pipelines sourcing data from internal and external systems, transforming the data for the optimal needs of various systems.
  • Orchestrate: design and implement integration and black box tests to ensure the source to target mapping is implemented as expected by the Data Pipelines.
  • Ensure you helm; build analytics tools that utilize the Data Pipelines to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
  • Help maintain data lineage allowing users to trace data reliably to the point of origin and pinpoint any quality problems in the Data Pipelines.
  • Develop and maintain scalable Data Pipelines which transform all internal data to empower every part of your organization to make informed decisions.
  • Be accountable for introducing and applying cutting edge technologies and techniques around big data, distributed systems, analytics, microservices, Data Pipelines, and observability.
  • Be accountable for designing, implementing, and maintaining data warehouses and near real time Data Pipelines via the practical application of existing and new data engineering techniques.
  • Pilot: partner with data engineering and business technology teams to build high quality and high scale Data Pipelines and assets that facilitate fast and reliable reporting.
  • Organize: design, build and launch efficient and reliable Data Pipelines for ingesting and transforming data from internal and cloud applications.
  • Be accountable for choosing the best tools/services/resources to build robust Data Pipelines for data ingestion, connection, transformation, and distribution.
  • Coordinate: design, build and launch efficient and reliable Data Pipelines in order to source data from complex and disparate data sources, and process that data into consumable formats that help to enable insights.
  • Audit: guarantee compliance with data governance and data security requirements while creating, improving and operationalizing integrated and reusable Data Pipelines.
  • Methodize: effectively acquire and translate user requirements into technical specifications to develop automated Data Pipelines to satisfy business demand.
  • Enable data access, data processing, and data products by architecting, maintaining, scaling, monitoring and securing Data Warehouse, EL and ETL system, and Data Pipelines and BI systems.
  • Systematize: design and implement secure Data Pipelines to prepare, process, ingest and organize data into data data lake / data warehouse from disparate on premise and cloud data sources.

 

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