Top Remote Data & Analytics Jobs in Houston, TX

Designs, develops, and optimizes high-availability relational databases supporting high-volume logistics services. Responsibilities include schema and database object architecture, SQL and T-SQL development, stored procedure implementation, performance tuning, scalability improvements, code reviews, prototyping, and end-to-end feature delivery. The role collaborates with engineering and infrastructure teams and may support database administration, cloud migrations, and modern data platforms.

Build and maintain streaming, replication, and data platform pipelines using Kafka, CDC, and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability across cloud environments. Participate in on-call rotations, incident response, design reviews, and technical documentation. The role requires a bachelor’s degree, five or more years of data engineering or backend engineering experience, and authorization to work in the United States without sponsorship.

Develop and enhance trade and commercial reporting solutions using Snowflake, Tableau, and related BI tools. Modernize manual and legacy reporting workflows into automated dashboards and scalable analytical solutions. Translate queries and business rules across data platforms, validate reporting accuracy, investigate discrepancies, document workflows, and partner with commercial and senior stakeholders to deliver actionable insights.

Develop and enhance trade and commercial reporting solutions using Snowflake, Tableau, and related BI platforms. Modernize manual and legacy workflows into automated dashboards, migrate reporting logic across data warehouses, validate data accuracy, investigate discrepancies, document processes, and collaborate with commercial and technical stakeholders to deliver actionable reporting.

Leads enterprise information architecture, business intelligence, and data warehouse strategy and delivery. Oversees BI and data warehouse teams, establishes roadmaps and governance, mentors technical staff, manages project priorities, and ensures high-quality implementation. Designs enterprise data models, datamarts, metadata, ETL processes, reporting strategies, and integration artifacts. Partners with business and technology stakeholders to align data solutions with strategic goals, improve adoption, track value, and drive continuous improvement.

Analyze quantitative and qualitative customer data using Adobe Customer Journey Analytics and BI tools. Build data pipelines, segments, calculated metrics, reports, and executive dashboards; validate deliverables; identify business insights; and communicate recommendations to clients and stakeholders. The role also involves project planning, process improvement, web analytics debugging, and presenting client-ready findings.

Analyze quantitative and qualitative customer data using Adobe Customer Journey Analytics and BI tools. Build data pipelines, segments, calculated metrics, dashboards, reports, and executive presentations. Translate findings into actionable business recommendations, validate deliverables, support client decision-making, and improve analytics processes. The role also requires managing project plans, collaborating across teams, presenting insights, and debugging webpages to verify analytics data collection.

Analyze complex datasets to identify trends, patterns, anomalies, and business opportunities. Develop KPIs, perform root-cause analysis, build Power BI dashboards using DAX, SQL, and Power Query, validate data quality, automate reporting, and translate findings into actionable recommendations for business stakeholders.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.