What jobs exist in open data platforms?

The landscape of employment surrounding open data platforms is surprisingly varied, stretching far beyond the typical image of a lone coder publishing datasets. As data becomes the currency of modern governance, technology, and social impact initiatives, specialized roles have emerged to manage, secure, interpret, and deploy this information responsibly. These careers span from the physical hardware that houses the data to the high-level strategy dictating its use, offering paths for technicians, product managers, researchers, and executive leaders alike.

# Builders AI

In the most technologically advanced sectors, often centered around artificial intelligence research and deployment, jobs focus on creating the platforms that allow models to access and process massive amounts of information reliably and at scale. These positions frequently sit within companies pushing the boundaries of machine learning and large language models.

A key role here is the Product Manager, Data Platform, responsible for designing the tooling and integrations that connect sophisticated external enterprises and developers to core AI capabilities. This job requires a blend of deep technical understanding of the enterprise data stack, strong business acumen, and curiosity about research breakthroughs. The goal is to make the complexity of running world-class AI systems manageable for the end-user.

Deeper in the engineering stack, job boards for Open Data Science frequently list highly specialized technical roles. These include Data Scientists working on predictive analytics, MLOps Engineers ensuring machine learning workflows are production-ready, and Distributed Systems Engineers managing the architecture for scale. A Data Engineer in this environment is focused on building and managing the practical pipelines—deploying technologies for collection, storage, and optimization. These roles often command premium compensation; for instance, a Product Manager position at a leading AI lab was advertised with an offer near $405K plus equity.

# Physical Layer

Before data can be analyzed or productized, it must reside on physical servers, managed within data centers. These roles are less about the data content and more about the physical platform itself. Entry-level positions here, such as Data Center Technician, often involve manual, hands-on tasks. Daily duties can include responding to tickets for powering equipment on or off, racking and unracking hardware, replacing failed components like hard drives or PCI cards, and meticulous cabling.

These jobs require physical aptitude, often noting the need to lift 50 pounds, though specialized server lifts assist with the heaviest gear. While the work can be intense during major installations, there are quieter periods, sometimes on night shifts, where technicians might manage inventory or desk work. Career progression often looks toward Data Center Engineer roles, which involve system design, layout planning, and network provisioning. A significant current consideration in this field is the transition of companies to cloud infrastructure, demanding that these infrastructure professionals adapt to new, cloud-centric skill sets. The environment itself is notoriously challenging, described by some as "cold and loud," or depending on the cooling strategy, occasionally "hot and loud".

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# Advocacy Roles

A third major category exists within non-profit organizations and international bodies where the mission is explicitly the promotion and impact of open data for social good or development. These jobs often differ significantly from the high-salary tech track, focusing instead on methodological rigor, policy influence, and community building.

Organizations like Open Data Watch (ODW) hire Researchers for projects like the Open Data Inventory (ODIN), which involves remotely assessing national statistical offices across the globe against an openness methodology. This work is contract-based, typically requiring a strong degree in development or statistics, excellent attention to detail, and often proficiency in languages beyond English, with pay rates explicitly stated, such as $19-$21 per hour. At the leadership level in this sphere, a Program Manager role demands substantial experience (five to eight years) managing global, multi-stream projects, securing donor investments, and translating technical findings into policy influence, with salaries typically ranging from \90,000 to \105,000.

Contrastingly, technical roles within open data advocacy groups, such as those at Open Data Services Co-operative, center on building open-source tools for transparency initiatives like Open Contracting. They seek Python Software Developers and Data Visualisation Web Developers, operating within a worker co-operative structure where employees become joint owners after a probationary period, with compensation including a salary (e.g., £43,120 FTE) plus profit share. This structure itself is a significant differentiator from typical corporate employment models.

# Governance Policy

When open data is released by federal or large governmental bodies, the immediate focus shifts heavily toward structure, security, and compliance. The roles advertised by specific federal data job connectors reveal a need for specialists in administrative and strategic data management rather than just analytical science.

Key roles here include the Data Governance Specialist, who designs and implements policies for data quality, compliance, and lifecycle management to align with strategic objectives. The Data Strategist develops the overarching data utilization plan for agencies. Further up, Program Executive roles act as Chief Data Officers (CDOs), focusing on enterprise-wide modernization, policy development, and cultural adoption of data use. There is also high demand for Data Architect positions, ensuring complex data infrastructures are correctly modeled and maintained for agency-wide efficiency. These positions emphasize adherence to federal regulations and best practices for data integrity.

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# Curation Quality

Bridging the gap between raw infrastructure and high-level analytics is the critical function of data quality and curation, exemplified by roles like the Data Associate at organizations focused on specific sector data, such as supply chains. This role is fundamentally about ensuring the usability of the platform’s data, which is often crowd-sourced.

A Data Associate dedicates a majority of their time (around 60%) to Data Management & Quality Assurance. This is highly detailed work: reviewing, cleaning, merging, and deduplicating facility records (like names and addresses), and verifying geospatial coordinates using specialized tools. They also label data for machine learning training. This position requires familiarity with basic data analysis tools like Excel, Python, or SQL, and typically asks for 1-2 years of relevant experience, often in a remote setting. The salary range, \50,000 to \55,000, reflects a professional, entry-to-mid-level technical position focused on data integrity rather than complex modeling.

If you look closely at the skill sets required across these distinct sectors—from the high-level algorithmic thinking of the Data Scientist to the meticulous, detail-oriented auditing of the ODIN Researcher—a pattern emerges where attention to detail is the non-negotiable common denominator. A significant insight for anyone navigating this job market is that the career ladder is not strictly vertical; it’s frequently horizontal, with professionals moving between domains based on their affinity for policy versus engineering. For example, a Data Engineer building a massive pipeline in a tech company might pivot to become a Data Architect in the Federal sector, simply swapping the purpose of the data (AI optimization versus regulatory compliance) while retaining core skills in data modeling and infrastructure.

Another subtle but important distinction lies in the organizational model supporting the data. In the pursuit of transparency and ethical data use, worker co-operatives dedicated to open data have emerged, offering profit-sharing and joint ownership alongside technical development roles. This contrasts sharply with the centralized, high-compensation, high-expectation environments found in major proprietary data platforms, which often seek to build proprietary data integrations for model training. Understanding who is funding the data platform—be it venture capital, government grants, or a membership model—will heavily influence the required job focus, leaning toward rapid feature deployment in the former and long-term governance in the latter. For those starting out, foundational skills in database querying (like SQL) and basic statistical literacy are more widely applicable across all listed domains, from Data Associate to Data Scientist, than highly niche programming languages.