Company Overview

At Groupon, we are dedicated to recognising and rewarding those who drive impactful change. We believe in making swift, impactful decisions without getting bogged down in endless meetings. Here, your advancement is based on your performance and contributions, not politics. We thrive on innovation, embracing cutting-edge technology to stay ahead of the curve.

If you're seeking a role where growth opportunities are limitless and your ability to deliver is truly valued, this is the place for you. Our culture is built on agility, transparency, and resilience. We prioritise quick decision-making, open and honest leadership, and merit-based advancement. We empower our teams to overcome obstacles and take full ownership of their work, driving real results.

Join us on our transformation journey! If you're ready to be part of a forward-thinking team that celebrates hard work and rewards success, we want to hear from you. Read more.

Role overview

The Data Platform Architect is responsible for designing, building, and maintaining the data platform infrastructure and architecture within Groupon. This role involves understanding business requirements, selecting appropriate technologies, and developing scalable and reliable data platforms to enable data ingestion, storage, processing, and analysis. The Data Platform Architect collaborates with cross-functional teams to ensure the availability, performance, and security of the data platform while adhering to best practices and industry standards.

Job Responsibilities

1. Data Platform Strategy and Architecture:

• Develop and maintain the organization's data platform strategy and roadmap.

• Define the overall architecture, components, and technologies required for the data platform. 

• Identify and evaluate emerging data technologies and trends to inform architectural decisions.

• Propose innovative solutions and improvements to the existing data platform.

2. Data Ingestion and Integration:

• Design and implement robust data ingestion processes to acquire data from various sources (internal and external).

• Define data integration patterns and technologies to ensure smooth data flow into the platform.

• Establish data quality checks and validation mechanisms during the ingestion process.

3. Data Storage and Management:

• Determine appropriate data storage technologies and structures (e.g., databases, data lakes, object storage) based on the organization's needs.

• Design and implement scalable and reliable data storage solutions.

• Develop data management strategies, including data partitioning, indexing, and archiving, to optimize performance and storage efficiency.

4. Data Processing and Analytics:

• Design and implement data processing frameworks and pipelines to transform and analyze data at scale.

• Select and configure appropriate processing technologies, such as distributed computing platforms, data processing frameworks, and streaming systems.

• Collaborate with data analysts and data scientists to ensure the platform supports advanced analytics and machine learning workloads.

• Review and propose real-time data processing capabilities if appropriate

5. Data Security and Governance:

• Define and implement data security measures, including data access controls, encryption, and data masking.

• Establish data governance frameworks and policies to ensure compliance with data regulations and privacy requirements. 

• Monitor and optimize data platform performance and availability, including disaster recovery and backup strategies.

6. Collaboration and Stakeholder Management:

• Collaborate with cross-functional teams, including data engineers, data scientists, and business stakeholders, to understand their requirements and align the data platform architecture accordingly.

• Act as a subject matter expert on data platform architecture and provide guidance and support to the development team.

• Facilitate communication and knowledge sharing among technical and non-technical stakeholders.

7. Contribute to the building of an effective, high-quality, fit-for-purpose Data Management department

• Managing consultants or specific suppliers attracted to deliver on above responsibilities.

• Collaborate with other teams to ensure the successful delivery of key projects (including the data strategy, migration from Teradata, adoption of data products, project management standards, data catalog, the development of appropriate roadmaps, business value formulation, and feasibility analysis of product formulation).

• Cultivate an innovative working environment by supporting creativity, continuous improvements, and high-performance teamwork.

• Create a working environment that strengthens team empowerment, increases staff growth, motivation and job-satisfaction. Provide regular constructive feedback and support professional development.

• Consult and advise stakeholders, including regional and divisional leaders; share expertise and knowledge freely as and where required. Provide training, documentation, etc., when required.

• Ensure availability and development of standard operating procedures and/or guidelines and required documentation is delivered and where relevant available into the Service Management Tool.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field.

• A minimum of 5 years in data platforms management and architecture

• Proven experience as a Data Platform Architect. 

• Strong knowledge of data platform technologies, including data ingestion, storage, processing, and analytics. Migration experience from Teradata to Google Big Query is a big plus.

• Proficiency in database design, data warehousing, and data integration concepts

• Proficiency in cloud platforms and technologies, such as AWS or Google Cloud.

• Experience with big data technologies, such as Hadoop, Spark, and distributed storage systems.

• Experience with programming languages - SQL, Python, Scala

• Familiarity with data governance, data security, and data privacy regulations (e.g., SoX, GDPR, CCPA)

Skills and competencies

• Strong strategic and conceptual thinking; setting meaningful, long-term vision and strategy, consider long-term potential, propose challenging strategic goals. 

• Propensity for embracing change and ambiguity: anticipate emerging conditions and demands, embrace widespread organizational change, ability to navigate global and complex environments

• Dynamics - view uncertainty and disruption as an opportunity. 

• Ability to drive results, and create culture that fosters proactive action, actively prioritizes, set high standards. 

• Developing others: Push autonomy and empowerment, view people development as imperative, create a culture of accountability.

• Data products development, business value development, data product deployment, and resource mobilization.

• Strong presentation, written and oral communication skills.

• Able to network effectively and influence and inspire others including peers, the membership and other stakeholders. 

• Focused on quality and standards, results, and accountability.

• Excellent interpersonal skills; proven people’s management skills (staff and consultants), including conflict resolution.

• Proven training, knowledge transfer and supervisory skills as part of the people’s management.

• Proven teamwork and trust-building skills, including development of effective and efficient networks and partnerships within and outside of the organization.

• Proactive approach to finding creative and constructive solutions to difficult issues.

• Fluent spoken and written English

Values & mindset

Values: Entrepreneurship mindset, Ownership and accountability, Continuous improvement, Product management, DevOps mindset
Core competencies: Ability to make decisions and articulate rationale behind them, Communication; Collaboration and teamwork; Judgment and decision making; Creativity and innovation; Building trust, Ability to articulate complex problems
Functional competencies: Strategic orientation, Leadership, Empowering and influencing others

Our stack

Data platforms: Teradata, Datalake on GCP, Google Big Query, Kafka

Data integration tools: Spark, Google DataProc, Teradata TPT, Google Datastreams, Keboola, Quite a few “homegrown” tools

Reporting tools: Tableau