Head of Data Operations - London, United Kingdom - QS Quacquarelli Symonds

Description

Why QS?

At QS, our culture fosters a flexible working environment that encourages our employees to own their career whilst personally and professionally thriving.

We collaborate, respect and support each other - and as a company, our values underpin these.

Our mission is to empower motivated people around the world to fulfil their potential through higher education, and to ensure everyone has the same opportunity to achieve that potential.

We celebrate the diversity of our teams and believe through sharing our experiences we can learn from one another and become stronger together, which enables us to achieve our goal.

At QS, you'll be responsible for implementing real change in the international higher education landscape, full of exciting and interesting challenges where you can drive positive impact across the business.

In October 2023 we were thrilled to be included in Newsweek's Top 100 Most Loved Workplaces in the UK list which is compiled using employee sentiment and satisfaction data.

Using insights from more than two million employees in organisations with 50 to 100,000 members of staff, the list recognises workplaces where employees feel most respected and appreciated.

The role

In 2022, QS formed a newly central Data and Analytics function, with investment made into latest cloud-based infrastructure and tools for data mastery.

Our Data and Analytics team is central to the continued growth transformation of QS.

As a division, we're supporting the transformation of the business by evolving from being focused on infrastructure and tools to becoming a hub of analytical brilliance for the entire organisation.

We need a technical leader who can take on the role of Head of Data Operations and guide us through this transformation while ensuring the current data operations processes continue to deliver value on time and to the requisite quality as we progress through this change.

Role responsibilities:


_ Core responsibilities_
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Data collection and enrichment.

Extracting from both in-house and third-party sources, testing against agreed standards, transforming to an agreed format and loading into an agreed storage of core institutional performance data.

Providing constructive feedback to the owners/representatives of the corresponding data sources on the requirements of data collection and validation process.

Setting requirements and quality standards for other teams responsible for supporting the data scraping from public sources.
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DataOps Research & Development.

• Technical support of business analysis, mathematical and statistical modelling, rankings models design simplification, automation, building technical roadmap, and driving ranking delivery workflow improvements;
• Taking a critical look at QSs existing evaluation models, streamlining work production processes, procedures including quality control;
• Supporting technical migration of existing data assets into the QS Snowflake environment
• Supporting data engineering requirements (ranking methodology script rewrites for all assets including SQL, Python, R, and spreadsheets into a Snowflake environment)

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Rankings production.

Executing all necessary production steps to produce the results of institutional evaluations (rankings and their corresponding performance indicators) on time and of an agreed quality.

Collaborating with a global team of engineers, product managers, data analysists to ensure a smooth running of ranking operations.
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Reports and delivery.

Developing, maintaining and running existing routines for generating various PDF reports, Tableau dashboards and different formats of rankings results needed to be delivered to final stakeholders (institutions, website users, internal stakeholders, partner organisations, clients etc.).

Consult with both internal and external stakeholders, providing key analytical insights and communicating our data work clearly.
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Customer/stakeholder support.
Answer advance customer/stakeholder's support questions (L3-L4 support levels) either directly or by upskilling/training other teams from L1-L2 support.
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Quality Assurance.

Design new and execute existing QA standards and procedures to make sure that both underlying data and any intermediary or final rankings deliverables are of a satisfied level of quality.

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Team Leadership.

Leading a team of data analysts, data engineers and data scientists, being responsible for technical aspects of data collection and enrichment, DataOps R&D, rankings production, reports and delivery (including project planning, breaking down of work, orchestration of all steps of the production flow).

_ Daily Responsibilities:
_

• Data collection (automation of certain types of data collection and data validation).
• Data engineering (developing and maintaining data pipelines, prepare data for data analysis).
• Data analysis (compile rankings results and analyse them).
• Planning (building project plans, brea