A data architect plays a crucial role at LinkedIn by designing, developing, and maintaining the overall data infrastructure. This involves working with massive amounts of data and ensuring it is reliable, accessible, secure, and able to meet current and future business needs. Some key responsibilities of a LinkedIn data architect include:
Defining Data Architecture
A LinkedIn data architect starts by understanding the company’s overall business goals and data needs. They then define a scalable data architecture that aligns with these goals. This involves choosing technologies and designing databases, data pipelines, APIs, data models, and more. The architecture must support accessing, processing, and analyzing large data sets in a cost-effective manner.
Selecting Data Storage Systems
LinkedIn has enormous amounts of data from over 740 million members. A data architect evaluates storage systems like Hadoop, relational databases, NoSQL databases, data warehouses, and data lakes. They choose appropriate solutions to store and manage different types of data based on access speed, scale, cost, data structure, and other requirements.
Building Data Pipelines
Data architects design and implement data pipelines to move data between systems. For example, extracting data from transactional databases, transforming it, and loading it into data warehouses and analytics platforms. The pipelines need to process different types of data, handle large volumes, deliver real-time or scheduled data, and ensure reliability and data quality. Data architects leverage tools like Apache Kafka, Spark, and Airflow to build robust pipelines.
Creating Data Models and Schemas
A key task is developing data models and database schemas optimized for analytics and business intelligence. The data architect designs logical and physical data models that standardize data and define structures in databases and data warehouses. This involves working with stakeholders to identify types of data and relationships between different data entities.
Monitoring Data Infrastructure
Data architects are responsible for monitoring the performance and efficiency of data infrastructure. This includes tracking key metrics like pipeline data volume and throughput, database query response times, and data warehouse utilization. Architects need to optimize data solutions and troubleshoot issues like bottlenecks.
Ensuring Data Security and Compliance
Sensitive member data must be properly secured and access controlled. Data architects build security measures into the architecture, such as encryption, access roles, and network segmentation. They also stay current on regulations like GDPR and ensure LinkedIn’s data platforms facilitate compliance.
Supporting Analytics Initiatives
Data architects collaborate with data scientists, analysts and engineers to understand analytics use cases. They provide guidance on available data, help access the appropriate data, and ensure infrastructure can support analytics at scale. This enables using advanced techniques like machine learning and AI.
Leading Data Governance
LinkedIn data architects institute data governance frameworks about policies, standards, and accountabilities. This helps align practices across teams for managing data as an enterprise asset. Governance also helps improve data quality, security, lifecycle management and reduces risk.
Enabling Access to Authoritative Data
Data architects implement data catalogs, APIs, reporting tools, and self-service interfaces. This makes it easy for business teams to find the most authoritative and reliable data when they need it and fosters a data-driven culture.
Maintaining Existing Systems
Data infrastructure requires ongoing management. Architects monitor usage across systems like data lakes and warehouses. They tune performance, expand capacity, upgrade technology, and migrate data as needed. Data architects also fix issues that arise across the infrastructure.
Innovating Advanced Data Solutions
Data architects research technology trends and evaluate emerging data tools. They incorporate innovative solutions like graph databases, streaming analytics, and predictive modeling as appropriate. Adoption of cutting-edge technology enables advanced use cases.
Collaborating Across Teams
Data architects serve as a bridge between technical data teams and business leadership. They align data infrastructure roadmaps with business goals and communicate capabilities and tradeoffs. Data architects also provide guidance to engineering teams building data-driven products and analytics models.
Key Skills for a Data Architect at LinkedIn
Here are crucial skills needed to thrive as a data architect at a company like LinkedIn:
- Expertise with data platforms like Hadoop, Spark, relational and NoSQL databases
- Experience with data integration, messaging, orchestration, and pipeline tools
- Knowledge of distributed computing and large-scale data processing
- Understanding of data modeling, structure, storage, and access techniques
- Ability to design secure, scalable, and reliable data architectures
- Passion for staying updated on data technologies and techniques
- Systems thinking and problem solving skills
- Strong communication and collaboration abilities
Educational Background
Most data architects have a bachelor’s degree in computer science, information technology, software engineering or a related field. Advanced degrees like a Master’s in data science are increasingly valued as data becomes more complex and integral to strategic decisions.
Certifications
Certifications can demonstrate skills and experience in areas like:
- AWS Certified Solutions Architect
- Cloudera Certified Associate Data Engineer
- GCP Professional Data Engineer
- Microsoft Certified Azure Data Engineer Associate
- Oracle Database SQL Certified Expert
Job Growth and Salary Outlook
Data architect roles are seeing strong demand given the strategic importance of data. According to Glassdoor, average salaries range from $120,000 to $190,000. Location, experience, and specific skills impact compensation. For example, data architects in major tech hubs like the San Francisco Bay Area tend to earn towards the higher end.
The U.S. Bureau of Labor Statistics groups data architects with database administrators. This occupation is projected to grow 10% between 2020 and 2030, faster than the average across all occupations. Demand is being driven by the popularity of data-driven decision making and the need to manage expanding data volumes and complexity.
Conclusion
Data architects play an integral role at companies like LinkedIn by designing, implementing, and governing the infrastructure powering data solutions. They require a blend of technical skills, business acumen, and communication ability to bridge the gap between data capabilities and company goals. As data becomes even more vital for competitiveness, skilled data architects will continue to be in high demand across many industries.
