• Own data pipeline design and implementation; consolidate 250+ transactional tables into ~10 analytic tables; move from weekly full-refresh to daily/near-real-time incremental loads via CDC (e.g., AWS DMS). Build and own the centralized analytics layer. • Establish unified data definitions, metrics, KPIs, and benchmarks across the organization. Deliver a Master Data Management (MDM) framework with unified definitions and governance standards. • Drive migration to the centralized layer while deprecating direct raw table access. • Partner with internal departments to drive self-service analytics and build aggregations/data marts for benchmarking and experimentation. • Conduct exploratory and statistical analysis for data validation, and implement data observability/monitoring. • Partner with the Applied AI Engineer to design AI-ready data models (including retrieval- and embedding-ready structures to ground RAG and LLM applications). Expand the data lake to ingest third-party sources (e.g., HubSpot, Jira, Zendesk).

Role Description - Monitor and support daily ELT pipelines and reporting processes. - Design, document, and maintain data warehouse models as source systems evolve. - Develop and maintain semantic-layer and business intelligence data models. - Support month-end reporting and recurring business processes. - Improve and prepare data assets for AI-enabled workflows and automation initiatives. - Design data transformations that enhance data quality, consistency, accessibility, and usability. - Troubleshoot data issues and support analytics requests across the organization. - Serve as the primary technical resource for analytics infrastructure and reporting systems. - Prepare internal metrics, analytics, and leadership reporting. - Collaborate with stakeholders on data-driven initiatives and continuous improvements. Qualifications - 4+ years of experience in analytics engineering, data engineering, business intelligence, analytics, or related fields. - Strong understanding of data warehousing, dimensional modeling, and semantic-layer concepts. - Experience building and maintaining ELT/ETL pipelines. - Strong SQL and data modeling skills. - Experience working with software engineering workflows and source control. - Strong communication, documentation, and stakeholder management skills. - Hands-on experience with dbt, Amazon Redshift, Amazon QuickSight, AWS, Python, GitHub, Docker, and SQL. - Advanced English. Requirements - Experience with CI/CD workflows. - Knowledge of data governance practices and experience in healthcare, finance, or other regulated industries. Benefits - Great Place To Work certification. - A company with more than 15 years of experience. - Work with world-class clients and long-term projects. - English scholarships for an external institute. - English classes with company teachers. - State-of-the-art tools and resources. - Certifications for your professional growth. - Recreation and leisure activities. - Compliance with the regulations and labor rights of your region.

Role Description We are looking for detail-oriented Trainers to support an AI data annotation project. In this role, you will review pre-seeded questions paired with images and provide accurate "golden" answers based on what you observe in the image. This is a task-based, remote-friendly opportunity ideal for individuals who are meticulous and able to follow detailed instructions consistently. - Review a pre-seeded question along with an accompanying image (e.g., "What is the title of the Excel file based on what you see in the image?") - Carefully examine image content to identify the relevant details needed to answer the question - Provide a clear, accurate "golden” answer based solely on the visual information provided - Flag any images that are unclear, corrupted, or insufficient to answer the question - Maintain consistency and quality across a batch of tasks - Follow project-specific guidelines and rubrics as provided by the project team Qualifications - Native/Strong proficiency at Bengali - Fluent English proficiency (reading/writing) - Bachelor's degree required - Strong attention to detail and accuracy - Ability to follow written instructions precisely and consistently - Reliable access to a computer and internet connection - Prior experience with data annotation, data labeling, or quality review is a plus Requirements - Work Type: freelance, remote - Hours/Schedule: 10 hours per week - Compensation: $18/hr - Duration: Long-term Benefits - ✨ Limitless Flexibility: Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy. - 🌱 Limitless Growth: Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise. - 🌍 Limitless Support: Be part of a global contributor community with responsive guidance and support. - 💡 Real Impact: Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills. Company Description Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

• Work From Anywhere in LATAM and the Philippines • Design clean schemas, write efficient SQL, and optimize Postgres performance • Extend and evolve the Postgres schema across recruiting, candidates, and business operations • Build SQL views and derived tables for reporting and AI • Optimize query performance through indexing, partitioning, and execution-plan analysis • Complete schema audit with top-priority fixes shipped and measurable performance gains within the first 3-6 months