Top Remote Data & Analytics Jobs

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 enterprise data integration delivery and support, including APIs, event-driven integrations, batch processes, ETL/ELT workflows, data quality, documentation, and troubleshooting. Serves as a data integration subject matter expert and enterprise architect, partners with vendors and cross-functional teams, ensures data security and availability, manages multiple projects, and mentors junior staff. This is a remote role based in the EST or CST time zones.

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.

Leads complex business analyses using curated and aggregated DataMarts to generate actionable insights, identify growth opportunities, and address enterprise needs. Translates business requirements into analytics and reporting needs, develops visualizations and presentations, explains data anomalies, and advises stakeholders. The role provides subject-matter expertise, supports analytics delivery, investigates data issues, facilitates remediation, manages cross-functional relationships, and promotes compliance, risk management, and effective use of analytics in business decisions.

Design and architect robust ETL/data pipelines on cloud platforms. Build and automate data ingestion and preprocessing using Spark/Databricks, AWS Glue, Airflow. Implement warehousing architectures (EDW, DM, ODS, MOLAP/ROLAP), ensure SQL-based data quality, use version control, and deliver tested production pipelines while communicating with stakeholders.

Lead design and implementation of data architecture and reliable ETL processes to move data between heterogeneous sources and targets. Troubleshoot and resolve data architecture issues, collaborate with business analysts and technology teams, set up and test systems, and optimize ETL performance to support domestic resilience initiatives.

Build and maintain product analytics dashboards, validate and QA event tracking, translate event data into analytics-ready datasets, run funnels/cohorts/retention analyses, support A/B testing, and surface actionable product insights to Product, Growth, and Data stakeholders.

Lead and sustain enterprise master data operations (Material, Customer, Vendor, Pricing), enforce governance and SOX controls, drive data quality via KPIs and continuous improvement, support MDM/ERP initiatives, manage third-party providers, and develop data dictionaries, metrics, and process documentation.