Role Description We are looking for native or fluent language speakers to help train AI systems by reviewing and annotating content in their language (Turkish). This is a long-term, remote gig. No prior experience is required. On the job training will be provided. You will work on a variety of projects for our client that require native proficiency in your language. This could include but is not limited to: - Review content in your native language (e.g., newspaper articles, website screenshots, advertisements) - Draw bounding boxes around specific elements as instructed (e.g., highlight the commercial aspect of an article) - Answer structured questions about the content - Write short summaries when required - Follow clear annotation guidelines to ensure quality and consistency Qualifications - Native or highly fluent speakers of the target language - Students, freelancers, part-time workers, or anyone seeking flexible short-term work - People comfortable following written instructions on a computer Requirements - Strong reading comprehension in your language - Basic computer skills (no technical background needed) - Attention to detail - Ability to follow structured instructions Benefits - Duration: Minimum 6 months (Long-term Project) - Pay Rate: $3.5 USD / hour - Hours: 30–40 hours/week - Location: Fully remote - Work: Not voice-based — text and image annotation only - No Interview, only assessment: 15–20 minute language skills assessment

• You own our data warehouse and the reporting layer on top of it, setting patterns for how data is modeled, evolved, and exposed. • You write SQL and dbt models, refactor transformations, and build the tables and views downstream teams rely on. • You proactively engage with teams across the company to understand how data is created and used, identify gaps, and guide solutions. • You partner with our DevX and architecture teams on the boundary between product engineering services and Snowflake. • You build models, tests, and processes that anticipate malformed data and upstream changes, making our pipelines boring to operate. • You instrument what you own, define meaningful SLOs and data quality checks, and participate in our rotating on-call schedule. • You own and extend our Python jobs running on Glue, Lambda, and Step Functions. • You pair with more junior engineers on real work, raise the bar on PR and architecture reviews, and define the patterns and standards the team writes against.

• Develop and implement solutions for managing, processing, and analyzing large volumes of data. • These solutions will support both the feeding of Data Science and Artificial Intelligence models and direct data analysis by business users. • To achieve this, you will work closely with Data Science and Analytics teams and with business users, supporting them in their analyses. • Solutions involve handling large datasets, integrating diverse data sources and/or processing high-velocity data, aiming to create competitive advantages for the Company through intensive use of data.

• Acts as a subject matter expert and thought leader within the Data Platform Domain • Serves as a thought leader in data processing design and implementation, defining advanced structure for moving, storing, and maintaining high-quality data. • Leads projects by managing timelines, coordinating teams, and communicating project statuses. • Serves as a subject matter expert on governance standards, continuously aligning data practices with evolving industry best practices and requirements • Contributes to defining the overall vision and strategy for data engineering within the organization, ensuring alignment with organizational goals and long-term objectives • Establishes visionary goals, advises on strategic plans, employs advanced monitoring, influences high-level stakeholders, and delivers transformative results • Expansion of our Data Warehouse(s) and Lakehouse solutions for a healthcare data focused enterprise • Configuring and maintaining unity catalog to enable enterprise data lineage, data quality, auditability and data stewardship • Building out Data Security protocols and best practices including the management of identified and de-identified (PHI/PII) solutions • Always ensure a policy of least privilege is followed for anything being implemented • Building data solutions for clients while upholding the best standards for reliability, quality, and performance • Building solutions within Delta Live Tables and automation of transformations • Building out performant enterprise-level medallion architecture(s) • Building fit-for-purpose near real-time streaming and batch solutions • Building out performant and efficient enterprise solutions for internal and external users for both structured and unstructured healthcare data • Building out Infrastructure as Code using Terraform and Asset Bundles • Working with the business to build cost effective and cost transparent Data solutions • Help architect, build, and maintain robust and scalable data pipelines, monitoring, and optimizing performance • Identify and implement improvements to enhance data processing efficiency • Design and implement reliable and resilient Event Driven data processing • Lead design, implementation, and maintenance of standards based (FHIR, OMOP, etc.) and efficient data models for both structured and unstructured data • Assemble large, complex data sets that meet functional and non-functional business requirements • Develop and maintain data models, ensuring they align with business objectives and data privacy regulations • Partner internally and externally with key stakeholders to ensure we are providing meaningful, functional, and valuable data • Effectively work with Data, Development, Analysts, Data Science, and Business team members to gather requirements, propose, and build solutions. • Communicate complex technical concepts to non-technical stakeholders and provide guidance on best practices. • Ensure that technology execution aligns with business strategy and provides efficient, secure solutions and systems • Gather requirements and build out project plans to implement those requirements with forecasted efforts to implement • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc. • Build analytics tools that utilize the data pipeline to provide actionable insights into operational efficiency and other key business performance metrics. • Create data tools for clinical, analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader. • Lead investigation of new tooling, development implementation plans, and deployment of necessary tooling