Top Senior Data & Analytics Jobs in Houston, TX

Leverage advanced analytics, data manipulation, visualization, statistical modeling, predictive analytics, and machine learning to generate client insights and recommendations. Apply Adobe Experience Cloud solutions to support business transformation and strategic decision-making. Build client relationships, manage and inspire teams, navigate complex engagements, uphold professional standards, and deliver quality outcomes while developing technical and consulting expertise.

Leads business operations analytics, including strategic reporting, reporting-tool development, data integrity audits, operational trend analysis, and consolidation of data sources. Integrates budget and performance data to support financial planning and strategic decisions, streamlines business processes, evaluates system performance, and mentors analytical teams. Requires independent judgment and availability for nights, weekends, and variable schedules.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Lead Solutions Analyst translates complex business requirements into functional solutions for KYC and risk assessment initiatives, performs data analysis and mapping, prepares interface documents, supports UAT onboarding, coordinates with stakeholders, identifies risks, and promotes AI-assisted, reusable analysis practices.

Lead enterprise data architecture strategy and modernization across Microsoft Fabric, Azure, OneLake, lakehouses, warehouses, and integration platforms. Establish standards for modeling, ETL/ELT, governance, security, metadata, lineage, and data quality. Define AI-ready architecture patterns, lead architecture reviews, mentor engineers, and create reusable reference architectures. Drive migration from legacy platforms to Microsoft Fabric while promoting specification-driven development and scalable analytics, reporting, operational intelligence, and AI capabilities.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Design and maintain scalable cloud data pipelines, ELT/ETL workflows, data models, and automation systems supporting cloud cost visibility and optimization. Use Python, SQL, dbt, Airflow, AWS services, and Snowflake to process large-scale telemetry and usage data. Optimize performance, reliability, and cost while ensuring data quality, validation, auditability, and production troubleshooting. Partner with Engineering and Finance stakeholders on cost-aware decision-making.

Leads global master data quality and governance, manages onshore and offshore teams, and develops data quality standards, playbooks, dashboards, validation rules, and automated monitoring. Collaborates with business, IT, finance, sales, operations, and customer service stakeholders to resolve root causes, improve data integrity, support reporting, and maintain master data across enterprise systems. Serves as the organization’s data quality subject matter expert.

Designs, develops, and supports enterprise business intelligence solutions using Power BI and Qlik. Creates scalable dashboards, reports, and analytics to support business decisions; gathers stakeholder requirements; translates business needs into technical solutions; and maintains BI environments for performance, reliability, and data quality. The role may also apply AI-enabled analytics such as natural-language querying, anomaly detection, and automated reporting.

Designs, builds, tests, and supports enterprise data pipelines for acquired-company system integration and Snowflake archival. Translates business and technical requirements into mappings, transformations, quality controls, monitoring, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams to deliver governed, reusable data solutions. Supports migration, cutover, validation, documentation, hypercare, and operational handoff while improving automation, performance, cost efficiency, and data observability.

Designs and implements governed enterprise data pipelines supporting acquisitions, system integration, archival, reporting, analytics, and compliance. The role uses Snowflake to build scalable ingestion, transformation, validation, reconciliation, and archival solutions. It partners with architecture, security, infrastructure, application, business, vendor, and M&A teams; creates mappings and documentation; supports migration, cutover, go-live, and hypercare; and leads reusable, secure, observable data engineering practices.