Data Analytics Engineer (For independent contractors)

Role Description

As an Individual Contributor, you are responsible for the design and development of solutions that meet the analytical needs of the company by developing scalable, performant, and extensible data models and data processing pipelines. This drives efficiency, business velocity, and compliance across the company data estate.

You are responsible for the independent development and delivery of analytical data products that enable business decision-making. You will support solution ideation and technical designs, and drive hands-on implementation.

Key Objectives:

• Strategic Influence: Support, influence, and guide business and technology strategies as they relate to data through constant cross-functional interaction.

• Stakeholder Engagement: Actively engage with product, analytics, and commercial teams across wider tracks and verticals to deeply understand their business needs.

• Commercial Alignment: Identify critical data required to address business challenges by asking insightful questions, aligning transformation with the overall commercial strategy. This demands both technical expertise and an in-depth understanding of business needs.

Responsibilities & Duties

As a Data Analytics Engineer II, you are expected to operate independently across core execution areas while stepping up to own new domains and team culture.

Core Architecture & Execution (Independent):

• Metric Implementation: Drive the implementation of reliable, well-trusted metrics defined by the business, connecting disparate datasets into unified data products in the Lakehouse and/or Data Warehouse.

• Self-Serve Products: Produce curated, reusable analytical data products to enable self-serve analytics for many internal customers across departments.

• Data Modeling: Model data following best practices and Data Warehousing methodologies, such as Data Vault and (Kimball) Dimensional modeling.

• Insight Generation: Transform large, complex data sets into pragmatic, actionable insights and provide them in a consumable format for historical or predictive analysis.

• Data Governance: Perform technical stewardship, data classification, compliance management, data quality monitoring, and security considerations.

• Pipeline Health: Maintain and tune data pipeline health, including troubleshooting issues, implementing data quality controls, monitoring performance, and proactively addressing risks.

• Problem Resolution: Lead the technical resolution of problems and communicate them clearly to both technical and non-technical audiences.

• Domain Architecture: Support product teams in defining the Data Architecture for their domains, from conceptual to physical modeling in the Data Warehouse.

• Visual Validation: Build data visualizations to trial and validate the data products you build before releasing them to consumers.

Leadership & Collaboration (Experienced):

• Domain Ownership: Enter new areas, own the quality of the data, and work closely with product owners and scientists to develop the analytics backlog.

• Initiative & Scope: Work autonomously to self-steer initiatives, defining and breaking down work packages for junior members of the team.

• Data Culture: Drive the culture across the business unit for data quality, data governance, and data best practices.

• Community Growth: Actively contribute to the growth of the Data Engineering community at Booking.com through training, exploration of new technologies, interviewing, onboarding, and mentoring colleagues.

Skills & Experience

Background & Core Requirements:

• Experience: Minimum of 3 years of experience in a data or software-adjacent field, working with systems and data infrastructure at scale.

Technical & Professional Capabilities (Independent):

• Pipeline Design: Designing and implementing mature Data Warehouse pipelines using Data Vault and/or Dimensional modeling methodologies.

• Data Frameworks: Working with ETL/ELT tools and methodologies, alongside relational databases and any flavor of SQL in an analytical context.

• Orchestration: Working with workflow management and scheduling tools such as Apache Airflow or Argo.

• Code Quality: Writing and maintaining high-quality and reusable code, applying design patterns, and meeting strict coding standards.

• Storytelling & Alignment: Building data exploration/visualizations and designing impactful data storytelling.

• Communication: Excellent written and spoken communication skills with a proven track record of strong stakeholder management.

Preferred Technical Exposure:

• Data Environment: Practical experience working within a DevOps / DataOps environment.