Top Data & Analytics Jobs in Houston, TX

Build and operate scalable Python-first ETL and ELT pipelines for genomic and operational data. Integrate LIMS, laboratory instruments, applications, and third-party sources while guiding architecture and engineering standards. Own data quality, lineage, observability, reproducibility, audit readiness, security, and regulatory compliance across hybrid and cloud environments. Work with SQL databases, workflow orchestration, containers, Kubernetes, AWS data services, distributed processing, streaming, bioinformatics workflows, and infrastructure as code.

Leads district accountability analytics by forecasting TEA outcomes, analyzing STAAR and MAP performance data, and building predictive models, dashboards, reports, automated Python data pipelines, and audit-ready reporting systems. Supports executive decision-making, protects accountability ratings, and drives continuous improvement through accurate and timely student performance insights.

Advances master data quality, governance, stewardship, and AI readiness across Lower 48 business units. Profiles and troubleshoots data, defines quality standards and ownership, translates business needs into governed data and AI solutions, and supports responsible adoption of enterprise platforms. Partners with business, IT, analytics, and AI teams to deliver trusted data for analytics, automation, retrieval, and AI consumption.

Participates in a rotational data analytics internship involving research design, data collection, analysis, reporting, and executive brief development. The intern collaborates with cross-functional stakeholders to address enterprise insight needs, supports priority projects, communicates findings, and recommends improvements. Responsibilities also include learning through assignments, managing multiple activities, developing professional relationships, and receiving mentorship and feedback.

Own and mature the organization’s application and data governance program across Tekion, Snowflake, and Qlik. Responsibilities include administering Snowflake security and governance, defining trusted data structures and quality standards, managing Tekion configuration and integrations, documenting lineage and metadata, maintaining glossaries and inventories, supporting audit readiness and responsible AI, prioritizing governance requests, and educating stakeholders on data ownership and access controls.

Develop fundamental supply-and-demand models for global crude oil markets using econometric and statistical methods. Build Python and SQL analytics, visualization tools, data validation frameworks, and automated ingestion pipelines. Analyze market dislocations, disruptions, policy changes, and data releases to formulate probabilistic trade recommendations. Curate and transform proprietary commodity datasets, backtest signals, and present trade ideas to senior traders and portfolio managers.

Supports data governance for acquired-company integration and transformation initiatives. Responsibilities include inventorying structured and unstructured data, documenting ownership, classification, retention, legal holds, privacy, quality, metadata, lineage, and stewardship requirements. The role maintains catalogs and glossaries, supports source-to-target mapping and reconciliation, prepares governance metrics and dashboards, and coordinates with business, technology, legal, privacy, risk, security, and implementation teams to ensure data is trustworthy, compliant, and ready for migration, archival, modernization, and analytics.

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.