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Data Analyst

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Blockit Now Inc

2024-10-03 10:41:38

Job location Mc Kinney, Texas, United States

Job type: fulltime

Job industry: Administration

Job description

Job Description

The Data Analyst is a new role for blockit, joining the engineering team supporting healthcare based clients. This team has an ambitious remit to significantly impact the organization cross-functionally, ranging from client onboarding to schema normalization.

Data-driven transformation through the democratization and access of data via multiple platforms empowers our teams to make smarter decisions. Measuring the impact of those choices gives us the agility to adapt quickly when necessary. With the advent of these initiatives around data storage, centralization and automation, we anticipate massive gains in efficiency, growth in our core metrics, and a rising tide of excitement, morale and engagement.

You will use your passion for big data and analytics to provide insights to the business, responsible for conducting both recurring and ad hoc analysis for blockit's leadership team and internal stakeholders. As we build and integrate cutting-edge technology in data infrastructure, cloud services, artificial intelligence, augmented/virtual reality, and machine learning, we're excited to see blockit's growth and success.

Primary Responsibilities:

Interpret data trends and patterns in the organization using existing datasets to identify opportunities for improvement and drive growth.

Build, own and manage initiatives to develop and automate tooling, dashboards & training to communicate relevant growth metrics to internal stakeholders, on a regular cadence and ad-hoc basis.

Responsible for generating all recurring and ad-hoc reporting for Key Performance Indicators (KPI) and multi-channel performance reports for internal and external stakeholders.

Deliver reporting and analytics to cross functional leadership teams, presenting insights and recommendations from analyses, utilizing data to influence behavioral change in company decision making.

Collaborate regularly with the engineering team to review data insights, drive performance automation and improvements with data infrastructure, pipelines and applications.

Collaborate with each department, driving projects in the organization to identify opportunities for improvement, aligning recommendations to the company strategy.

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