Top Remote Entry Level Data & Analytics Jobs in CO

Performs accurate data entry, verification, updating, cleanup, organization, and maintenance across databases, spreadsheets, and digital systems. Maintains electronic records, converts documents into digital formats, prepares basic reports, retrieves information, tracks assignments, and supports scanning and filing. The role requires confidentiality, attention to detail, strong organization, dependable independent work, effective time management, and reliable remote-work capabilities.

Own data partner specification negotiations, data mapping, Ascend platform configuration, and internal QA across multiple implementations. Review file specifications, normalize schemas, validate test files, document data dictionaries, and route non-standard requests to Product or Customer Engineering. Coordinate with delivery and engagement teams on UAT, defects, readiness, and capacity constraints. Develop repeatable QA frameworks, implementation playbooks, procedures, and training materials while ensuring configurations meet quality standards before go-live.

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.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Investigates data governance issues using information management tools and data sources, identifies root causes, and partners with data owners, clients, vendors, and architects on resolution. Establishes governance baselines, standards, metrics, audits, and reporting. Defines governance requirements for data elements, integrates governance tests into ETL processes, communicates governance benefits, and drives continuous improvement of data management systems, policies, and processes.

The Data Analyst position requires U.S. citizenship and that all work be performed within the continental United States, Alaska, or Hawaii. The listed annual pay range is $90,000 to $110,000.

Develop and maintain Palantir Foundry data pipelines, transformations, Ontology models, workflows, data products, and user-facing applications. Integrate structured and unstructured data from enterprise systems and cloud platforms using Python and SQL. Implement data quality, governance, metadata, lineage, testing, and validation practices while collaborating with technical and business stakeholders. Support Agile development, documentation, modernization, AI-enabled analytics, troubleshooting, and federal data solutions.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Manage and mentor a data engineering team while leading Snowflake-based platform architecture, enterprise data integrations, governance, observability, and pipeline reliability. Partner with analysts, business leaders, product management, and engineering teams to improve trusted self-service data access. Establish engineering standards, modernize the data stack, optimize costs, and deliver an AI-ready data foundation supporting analytics, reporting, and future AI initiatives.