Top Remote Entry Level Data & Analytics Jobs in CO

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.

Enters, verifies, updates, and organizes information across databases, spreadsheets, and internal systems. The role identifies discrepancies, flags incomplete records, runs basic reports, maintains confidentiality, and documents recurring issues. Success is measured by accuracy, productivity, and turnaround time. Candidates need strong attention to detail, accurate typing, spreadsheet proficiency, basic computer skills, and the ability to perform repetitive work consistently.

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.

Annotate, label, review, and validate audio and video samples according to project guidelines. Identify and correct errors, document annotation decisions, resolve questions with project teams, meet accuracy and deadline requirements, and provide feedback to improve annotation workflows. Strong English communication, attention to detail, organization, and quality control are essential; familiarity with data-labeling tools is advantageous.

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.

Investigate and resolve data integration, file processing, and transfer issues. Monitor automated jobs, use SQL and SSIS to validate data, manage SFTP workflows, reprocess files, document integration processes, and collaborate with internal teams to ensure accurate and reliable client and vendor data exchanges.

Contributes to building and optimizing a large-scale data lake and reliable data pipelines for cybersecurity analytics. Responsibilities include data modeling, distributed processing, workload optimization, monitoring, observability, CI/CD, code reviews, and supporting MLOps. The role collaborates with senior engineers to improve processes and deliver cost-effective cloud data solutions.

Maintains accurate CRM data across HubSpot and ServiceNow by processing data requests, entering and cleaning records, resolving duplicates, managing account relationships, supporting bulk uploads, and addressing cross-system data issues. Partners with Sales, Marketing, and IT Development to ensure accurate integrations, workflows, deal data, and adherence to data processes.