Data Microstrategy Engineer

November 12

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Valtech

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Digital strategy • Experience & Commerce Platforms • Connected Experiences • Content & Campaigns • Data Science

5001 - 10000

Description

• Analyze a collection of raw data sets to create a meaningful impact on large enterprise clients while maintaining a high degree of scientific rigor and discipline. • Engineer data pipelines and products to help stakeholders make and execute data-driven decisions. • Communicate analytical findings in an intuitive and visually compelling way. • Creating highly visual and interactive dashboards via Tableau, PowerBI, or custom web applications • Conducting deep dive analysis and designing KPIs to help guide business decisions and measure success • Engineering data infrastructure, software libraries, and APIs supporting BI and ML data pipelines • Architecting cloud data platform components enabling the above • Building and tracking project timelines, dependences, and risks • Gathering stakeholder requirements and conducting technical due diligence toward designing pragmatic data-driven business solutions

Requirements

• Proven industry experience executing data engineering, analytics, and/or data science projects or Bachelors/Masters degree in quantitative studies including Engineering, Mathematics, Statistics, Computer Science or computation-intensive Sciences and Humanities. • Experience with the Microstrategy developer tool (as opposed to the web editor or workstation) • Ability to use the Microstrategy object manager to migrate objects between dev, UAT, prod environments • Understanding of the metadata architecture in MicroStrategy • Ability to create batch job scripts and monitor cube performance • Proficiency (can execute data ingestion to insight) in programmatic languages such as SQL, Python, and R. • Proficiency in visualization/reporting tools such as Tableau and PowerBI or programmatic visualization library such as R ggplot2, Python matplotlib/seaborn/bokeh, Javascript D3. • Proficiency in big data environments and tools such as Spark, Hive, Impala, Pig, etc. • Proficiency with cloud architecture components (AWS, Azure, Google) • Proficiency with data pipeline software such as Airflow, Luigi, or Prefect • Ability to turn raw data and ambiguous business questions into distilled findings and recommendations for action • Experience with statistical and machine learning libraries along with the ability to apply them appropriately to business problems • Experience leading and managing technical data/analytics/machine learning projects