Top Remote Data & Analytics Jobs in Huntsville, AL

Leads enterprise analytics strategy, measurement frameworks, KPI development, dashboards, reporting, and insights for B2B commerce, marketing, and customer experience clients. Oversees analytics implementations across web, commerce, CRM, CDP, BI, and data platforms; mentors analysts and consultants; manages data quality and governance; leads executive client discussions, workshops, proposals, and business growth opportunities.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Leads a team of data engineers responsible for building and scaling Niche’s data platform. Oversees data pipelines, warehouse layers, platform reliability, data quality, monitoring, and engineering best practices. Manages roadmap execution, Agile rituals, cross-functional collaboration, hiring, mentoring, and career development. The role also drives process improvements, operational excellence, scalability, and delivery of secure, observable data solutions supporting analytics, reporting, content, and operational systems.

Creates, analyzes, edits, and maintains GIS data supporting fiber and HFC network planning, design, construction, and regulatory map updates. Uses ArcGIS and ArcFM to manage geodatabases, spatial datasets, parcel data, route assets, and network schematics. Conducts map research, interprets technical records, supports GIS projects, develops mapping standards, and responds to internal requests. Collaborates with legal, market expansion, engineering, construction, and design teams while ensuring geospatial data quality and alignment with GIS best practices.

Own end-to-end data architecture, including ingestion, ETL, transformation, extracts, reporting, and dashboards. Lead DevOps and infrastructure for AI engineering systems, establish platform standards, and build reliable production infrastructure. Write and ship code, mentor engineers, and hire and lead a growing data and platform team. Partner with leadership to align architecture and platform investments with product, research, and business priorities.

Leads the full digital experience research lifecycle, including user interviews, testing, surveys, behavioral analysis, experimentation, and conversion optimization. Defines research and web measurement strategies, analyzes qualitative and quantitative data, builds dashboards and executive presentations, and translates findings into compelling data stories. Partners with marketing, technology, design, product, and development teams to improve global web experiences, while maintaining scalable research documentation and insight repositories.

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.