Top Remote Entry Level Data & Analytics Jobs in Boston, MA

Evaluate search engine results and provide feedback on text, audio, images, and videos to improve AI and online search experiences. Conduct online research, assess relevance and significance, maintain quality and accuracy standards, and complete assignments independently from a home office. The role requires English and Spanish proficiency, flexible part-time availability, completion of training and testing, and use of a smartphone, computer, reliable internet, and antivirus software.

Own Synera’s data warehouse and ingestion infrastructure, integrating GTM and business systems into reliable, documented, monitored pipelines. Build data models, reporting and forecasting layers, and agent-ready interfaces using APIs, MCP connectors, permissions, and governance. Develop retrieval infrastructure for documents and unstructured content, including chunking, embeddings, and indexing. Partner with teams across the company to improve data quality and make business information usable by AI agents.

Collect, clean, validate, and analyze operational data from multiple systems to support customer reporting and performance monitoring. Process SQL-based uploads, maintain accurate datasets, identify trends and anomalies, document reporting procedures, and create standardized workflows, dashboards, and tools. The role also supports process improvement and automation initiatives while collaborating with cross-functional teams and traveling to customer sites.

Supports the development and maintenance of Qlik dashboards, reports, and data visualizations. Performs data analysis, validates reporting outputs, identifies trends and anomalies, translates business requirements into reporting solutions, troubleshoots data issues, and helps maintain metrics and KPIs. Collaborates with business stakeholders and develops knowledge of organizational data sources, business intelligence practices, and data governance standards.

Design, build, and optimize scalable data pipelines using Databricks, PySpark, SQL, Delta Lake, and lakehouse architecture. Collaborate with analytics, product, and engineering teams while developing batch and streaming workflows, integrating data warehouse systems, and using cloud platforms and orchestration tools such as Azure, Airflow, ADF, or Prefect.

Reviews and annotates video content by identifying objects, people, actions, events, and visual details for AI training. Follows project guidelines, checks annotation quality, corrects inconsistencies, flags unclear content, meets productivity targets, and adapts to updated requirements. The role requires independent, detail-focused work, strong written English comprehension, basic computer skills, reliable internet, and personally provided equipment suitable for video annotation.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Evaluate AI-generated responses using a personal Gmail account and email history in English and Korean. Assess relevance, accuracy, helpfulness, personalization, and unsupported assumptions; compare responses and provide detailed feedback. Maintain privacy and confidentiality while working independently remotely. Requires a mixed-language inbox, approximately 1,000+ emails, reliable computer and internet access, at least four hours daily availability, and four hours of PST overlap for a 16-week contract.

Reviews and annotates Japanese-language text and images to help train AI systems. Responsibilities include drawing bounding boxes, answering structured questions, writing short summaries, and following annotation guidelines. This is a fully remote, long-term role requiring native or fluent Japanese, strong reading comprehension, basic computer skills, attention to detail, and reliable adherence to instructions. No prior experience is required; training and a language assessment are provided.

Evaluate AI-generated responses for relevance, accuracy, helpfulness, and personalization using a professional Google Workspace account and business email history. Compare responses, identify unsupported assumptions or personalization issues, provide detailed feedback, follow evaluation guidelines, and maintain confidentiality. Requires native German proficiency, an established business Gmail inbox, analytical attention to detail, independent remote work, and availability of up to 40 hours weekly with PST overlap.

Create and validate complex datasets for AI benchmark tasks, including files with malformed records, missing data, truncations, and inconsistent formatting. Define correct final states, write detailed grading rubrics, and assess AI outputs for accuracy and completeness. The role requires exceptional attention to detail, error detection, format reconciliation, accurate documentation, and clear asynchronous communication in regulated or audit-sensitive contexts.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.

Reviews and analyzes large business datasets for accuracy, completeness, relevance, privacy, and quality. Conducts document reviews involving PII, identifies data issues and anomalies, provides feedback to support AI training, refines quality assurance standards, and communicates findings to project stakeholders while maintaining data privacy and security compliance.

Build and maintain Snowflake and dbt data models using medallion architecture, CDC ingestion, star schemas, and slowly changing dimensions. Develop governed semantic datasets for BI and AI workloads, implement testing, documentation, monitoring, alerting, row-level security, and performance tuning. Partner with product and business stakeholders to translate reporting needs into reliable multi-tenant SaaS data solutions.