Data Engineer - Gender Intelligence Platform

JobTitle:Data Engineer

Before You Apply A Message from OurFounder.

For generations, women have navigated systems never built with them in mind. The gaps in leadership, health, wealth, and opportunity were engineered into the world we inheritedand now, for the first time, we have the tools to build something better. AI gives us the chance to design technology that truly partners with the women and organizations working tirelessly to advance women and girls. Being trusted to do this work is both a privilege and a responsibility, and I am looking for people who feel that responsibility as deeply as I do.

Uplevylis not a conventional workplace. Wehave a distinct culture. Weexist to create meaningful impact for society, and we hold ourselves to a standard of moving fast while delivering quality that endures. We partner only with those who share our commitment to doing important work, and we build technology that opens doors, expands opportunity, and enables more people toparticipateand benefit.

We hire only A-players and compensate them accordingly. We do notcompromise ontalent, and we part ways respectfully when performance does not meet the standards required by our mission. Everyone atUplevylwears multiple hats. No task is beneath anyone. Decisions are driven by merit, not hierarchy. We prioritize customers over internal convenience, avoid politics, andmaintainzero tolerance for unnecessary bureaucracy.

We move with urgency because the mission demands it. When the work calls for it, we stretch beyond traditional hoursnot out of obligation, but out of genuine commitment to building something transformative. We look for people energized by bold problems, people who find meaning in momentum and possibility, and who bring a proactive, high-ownership mindset to everything they do. If you thrive in environments where innovation, purpose, andhigh standardscome together, you will feel at home atUplevyl.

Key Responsibilities

• Design, develop, andmaintain scalable, resilient data pipelines ingesting data from 100+ global sources (World Bank, UN, OECD, national statistics offices, and more).
• Build real-time and near real-time data refresh systems to keep intelligence and insights updated across 50+ countries.
• Create robust ETL/ELT workflows capable of processing diverse data formats including APIs, CSVs, PDFs, semi-structured, and unstructured text.
• Implement enterprise-grade data isolation , multi-tenancy models, and secure data access architectures.
• Optimize vector databases and embedding pipelines for high-performance retrieval of gender-segmented insights.
• Design andmaintainscalable orchestration workflows using Airflow,Dagster, Prefect, or similar tools.
• Apply Clean Data Architecture principles such as Medallion Architecture, Data Vault 2.0, and domain-drivenmodeling.
• Ensure high data quality through validations, profiling, lineage tracking, and automated monitoring.
• Implement observability practices (logging, metrics, tracing) across data pipelines.
• Collaborate with AI/ML, engineering, and product teams to supply clean, reliable, and well-modeleddata for analytics and AI workloads.
• Maintain CI/CD automation for data workflows, schema management, and environment consistency.
• Review pipeline designs and contribute to best practices that enhance team efficiency, reliability, and scalability.

Performance Expectations

• Deliver highly reliable, scalable, and secure data pipelines within agreed timelines.
• Maintain exceptional uptime and freshness SLAs across global data pipelines.
• Proactivelyidentifybottlenecks in ingestion, transformation, storage, and retrieval layersand implement long-term architectural fixes.
• Ensure high data quality, accuracy, and consistency across all datasets.
• Take ownership from design through deployment, monitoring, and continuous improvement.
• Bring best practices that uplift engineering quality, data reliability, and team productivity.
• Communicate effectively with stakeholders, product teams, and AI/ML engineers to ensure smooth delivery of data products.

Qualifications

• 3+ years of experience building production-grade data pipelines at scale.
• Strong skills in Python and SQL , and hands-on experience with modern data stacks:
• Orchestration: Airflow,Dagster, Prefect
• Transformation:dbtor equivalent
• Warehouses/Lakes: Snowflake,BigQuery, Redshift,Databricksor Lakehouse platforms
• Experience with cloud platforms (AWS/GCP/Azure) and services used for large-scale data processing.
• Strong knowledge of ETL/ELT patterns, workflow orchestration, API integrations, and streaming pipelines.
• Experience handling large-scale datasets , high-ingestion throughput, and diverse pipeline workloads for multiple business domains.
• Exposure to Medallion Architecture, Data Vault 2.0, datamodelingbest practices , and schema evolution strategies.
• Deep data quality mindsetexperience with validation, testing, metadata, lineage, and anomaly detection tools.
• Experience with vector databases (e.g., Pinecone,Qdrant,Weaviate) and embedding pipelines is a strong plus.
• Curiosity and passion for turning messy public data into reliable and meaningful intelligence.
• Awareness, exposure, or hands-on experience with AI model integration, embeddings, fine-tuning pipelines, or LLM-driven data processing is a strong added advantage.
• Self-driven, detail-oriented, innovative, anda strong teamcollaborator with a passion for social impact.

Cultural &Work-Style Skills

1. Low Ego, High Contribution

You collaborate across disciplines, share knowledge openly, and welcome feedback on architecture, code, and decisions. You care more about building the right system than about being right.

2. Ownership From Design to Delivery

You think beyond tickets. You take responsibility for the full lifecycle of what youbuildincludingarchitecture, implementation, testing, reliability, and iteration. You solve problems end-to-end rather than waiting for perfect specifications.

3. Mission-Driven UrgencyWithTechnical Depth

You move quickly when needed, but never at the expense of long-term stability. You can distinguish when to build fast and when to buildrightand you communicate trade-offs clearly.

4. Agility in Ambiguity

You are comfortable building in evolving environments with partial information. You prototype, test assumptions early, reduce dependencies, and find creative paths through constraints.

5. Purpose-Fueled Resilience & Curiosity

You stay steady when debugging complex issues, you learn rapidly, and you adapt as the product and architecture evolve. You draw energy from solving meaningful problems and building systems that open doors for millions of women.