Top Remote Data & Analytics Jobs

Designs, develops, and maintains enterprise databases, ETL workflows, data platforms, reporting systems, and business intelligence solutions. Leads technical design, data integration, automation, troubleshooting, documentation, and production support. Collaborates with stakeholders and vendors to deliver scalable, secure, and reliable data solutions. Requires advanced SQL Server, SSIS, T-SQL, and SDLC expertise, with preferred experience in Microsoft BI tools, APIs, cloud data services, and Python or Java.

Develop and support analytical systems for operational decision-making, identify KPIs with business process owners, analyze data lifecycles, define data warehouse and ETL requirements, validate data engineering results, build dashboards, and present insights to customers. The role requires at least one year of data or BI analyst experience, strong SQL and data visualization skills, analytical communication abilities, and fluency in Russian and English.

Supports the Data team by monitoring incoming requests, selecting and running productionized jobs, checking outputs for accuracy, communicating status, and escalating out-of-scope work. The role emphasizes high-volume task execution, attention to detail, procedural consistency, professional written communication, and foundational command-line skills. A background check is required, with interest in progressive politics, voter data, and future growth into data services engineering preferred.

Design, build, and maintain ETL/ELT pipelines and data models, optimize tables/views for reporting, implement data quality checks and KPIs using SQL and Python, partner with BI analysts and stakeholders, migrate AWS/Athena data delivery to Databricks, and contribute to dashboard development for leadership reporting.

Leads audience research, advertising effectiveness, brand lift studies, and first-party audience analysis. Partners with Sales, Creative Strategy, Media Planning, and leadership to develop actionable insights, sales narratives, measurement strategies, presentations, and client recommendations. Manages research vendors, budgets, contracts, and methodologies while scaling research tools, dashboards, benchmarks, and best practices across the organization.

Collects and delivers market measurement data for in-store retail programs, supports test design and measurement planning, presents findings, and communicates insights to Sales and cross-functional stakeholders. The role manages multiple analyses and reports, ensures accuracy and timely delivery, and applies data interpretation, problem-solving, and stakeholder communication skills. Experience with analytics, consumer-packaged goods, media, agency environments, or Circana/NielsenIQ data is advantageous.

Designs, builds, and maintains enterprise lakehouse data pipelines, transformation layers, and data models. Develops scalable batch and streaming solutions using Spark, Kafka, and Flink; implements medallion architecture, data quality monitoring, and infrastructure as code. Troubleshoots performance and data incidents, participates in code reviews, collaborates on data modeling, and mentors junior and mid-level engineers.

Lead a Salesforce data engineering team to design, deliver, and support Salesforce-to-Salesforce integrations and migrations, data modeling, ETL/transformations, automation, and observability. Manage hiring, performance, and career development; partner with stakeholders; adopt AI/LLM agent tooling to automate workflows and improve data quality; reduce technical debt and document systems and processes.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.