Compensation: TBD, based on education, experience, role fit, startup needs, and interview process.
Job summary
As a Data Engineer, you will support one or more startups connected to Metasoft Capital by helping founders turn early ambition into practical execution. This role is built for remote startup environments where priorities can change quickly, information may be incomplete, and useful work depends on clear writing, steady ownership, and good judgment.
The role focuses on data pipelines, warehouses, schemas, governance, and reliable data movement. You may work with founders, product teams, technical teams, operations, customers, partners, or investors depending on the startup assignment. The goal is to help the company turn data, models, automations, and intelligent workflows into useful products and measurable business results, while building habits that make the team more reliable as it grows.
Core responsibilities
- Own the day-to-day work connected to data pipelines, warehouses, schemas, governance, and reliable data movement, keeping priorities clear and progress visible.
- Work closely with founders, managers, product, technology, operations, and customer-facing teams as needed.
- Create clear notes, decisions, requirements, checklists, or reports so work does not depend on memory or guesswork.
- Build reliable data, AI, or automation workflows that improve speed, accuracy, and business outcomes.
- Use practical judgment to balance speed, quality, cost, risk, and learning in a startup environment.
- Clarify the business question, user need, or workflow problem before selecting models, data structures, dashboards, or automation tools.
- Build data, AI, or automation assets that can be tested, monitored, improved, and explained to non-technical stakeholders.
- Identify data quality, privacy, accuracy, reliability, cost, and safety risks before they become product or customer problems.
- Create documentation for datasets, model assumptions, prompts, evaluation methods, workflows, and operating procedures.
- Design, build, test, review, and maintain systems with enough documentation that other team members can understand and improve the work.
- Participate in technical reviews, troubleshooting, reliability improvements, and practical decisions about build versus buy.
- Turn messy information into clear analysis, written findings, dashboards, recommendations, or experiments that help the startup make better decisions.
Education and experience requirements
- Education in data science, statistics, machine learning, computer science, mathematics, analytics, automation, or equivalent practical work is helpful.
- A degree is not the only pathway; strong projects, Kaggle-style work, GitHub examples, production automation, research, or startup experience may be considered.
- Experience with Python, SQL, APIs, data pipelines, cloud services, notebooks, ML workflows, model evaluation, or automation platforms is valuable.
- Ability to test assumptions carefully and explain technical results in simple business language.
- Understanding of data privacy, responsible AI, model limitations, and the difference between impressive demos and reliable production systems.
- Candidates should be ready to share examples of relevant work, projects, portfolios, case studies, writing samples, code samples, dashboards, campaigns, processes, or other evidence of ability when applicable.
Helpful skills and tools
- Python, SQL, notebooks, model APIs, vector databases, workflow automation tools, dashboards, cloud data warehouses, evaluation frameworks, and observability tools.
- Experience with data, analytics, AI systems, automation tools, Python, SQL, ML workflows, or related technical methods.
- Strong analytical judgment, curiosity, documentation habits, and ability to test assumptions carefully.
- Strong ownership mindset, reliability in remote work, and comfort working with incomplete information.
- Ability to communicate progress, blockers, and tradeoffs in a simple way for both technical and non-technical teammates.
- Curiosity, humility, and willingness to keep learning as the startup changes direction or grows.
- Clear written communication, organized files, practical documentation, and reliable follow-through are important because most teams operate remotely.
Remote startup working style
- Work asynchronously when possible, document decisions, and keep teammates updated on progress, blockers, risks, and next steps.
- Be comfortable with early-stage ambiguity, changing priorities, limited resources, and the need to learn quickly from customers and teammates.
- Use sound judgment to decide when to move fast, when to ask for help, when to slow down, and when to protect quality or trust.
- Respect confidentiality, founder context, customer information, and the trust required to support early-stage companies.
Success in this role looks like
- Reliable models, trusted data, useful automation, measurable efficiency gains, and evidence-based decisions.
- Models, dashboards, and automations are useful, trusted, and connected to real decisions or user value.
- Data quality and evaluation practices improve over time instead of staying hidden until something breaks.
- The startup learns where AI helps, where it does not, and how to build responsibly.
- The startup team can point to clearer execution, better documentation, stronger collaboration, and practical improvements connected to this role.
How to apply
- Email your resume/CV to Jobs@MetasoftCapital.com and include JID: 4857653 in the email subject line and at the beginning of your message.
- Use a subject line similar to: JID: 4857653 - Data Engineer - Resume/CV.
- Briefly explain why this role, a remote startup environment, and the relevant industry or function fit your experience, interests, and learning goals.
- If useful for the role, include links to your portfolio, GitHub, LinkedIn, website, writing samples, case studies, dashboards, design files, campaigns, or other work examples.