Top Data & Analytics Jobs in Dallas, TX

Leads risk reporting and analytics by synthesizing complex data from multiple sources into executive-ready narratives, presentations, and actionable insights. Advises senior leaders, identifies emerging risks and business implications, supports risk oversight and strategic decisions, and aligns stakeholders across Risk, Finance, Analytics, Operations, and Corporate Functions.

Owns AML transaction-monitoring models throughout their lifecycle, including performance monitoring, implementation testing, documentation, model changes, validation support, issue remediation, and retirement. Analyzes transaction data, model outputs, data quality, thresholds, segmentation, and risk factors using quantitative methods and programming. Translates AML typologies into measurable features, coordinates with technology, data, investigations, audit, and model-risk teams, and communicates model risks, findings, and remediation status to stakeholders.

Leads enterprise data engineering strategy, architecture, modernization, and governance. Designs scalable cloud-native data lakes, warehouses, lakehouses, ingestion pipelines, and real-time processing platforms. Establishes standards for data modeling, security, quality, observability, DataOps, DevOps, and compliance. Provides technical leadership across engineering teams, mentors senior professionals, drives transformation programs, and partners with business and technology stakeholders to deliver resilient data products supporting analytics, regulatory reporting, and operational intelligence.

Analyzes business performance, trends, competitor filings, and insurance product data for assigned states or product lines. Develops actionable recommendations, advanced queries, analytical tools, and strategic initiatives. Leads workstreams and low- to moderate-complexity projects, presents findings to product managers, supports countrywide implementations, and identifies improvements to property and specialty insurance offerings.

Leads client-facing data validation and risk workstreams, developing reliable data pipelines, integrations, transformations, and validation controls. Manages project plans, budgets, timelines, deliverables, and team performance while analyzing data structures and system interactions to identify risks. Coaches team members, collaborates with stakeholders, improves database and query performance, promotes automation and AI-enabled tools, and ensures technical standards, documentation, and independence requirements are maintained.

Leads enterprise-wide validation of analytical tools, including planning validation projects, assessing methodology quality and tool risk, researching best practices, and communicating findings through reports and presentations. Partners cross-functionally to influence business strategy, handles concurrent projects, develops solutions to business problems, and coaches junior staff. The role requires substantial data analysis, statistical or econometric modeling, large-scale data manipulation, open-source technology experience, and people management.

Design, build, and optimize end-to-end data pipelines and architectures using Databricks, Spark, AWS, Airflow, and Kafka. Develop ETL processes, data models, warehouses, and reliable Python and SQL solutions while ensuring data quality, security, and governance. Collaborate with data scientists, analysts, and stakeholders to support analytics and AI/ML initiatives. Monitor workflows, troubleshoot issues, validate AI-assisted outputs, and improve performance and resiliency.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

Leads enterprise data architecture initiatives by translating business requirements into scalable data designs, assessing impacts across systems and pipelines, modernizing legacy environments, and guiding AI/ML data solutions. Partners with data leaders and stakeholders, manages project plans, monitors implementation progress, and communicates risks and mitigation plans. Requires expertise in cloud and hybrid environments, data platforms, data engineering or architecture, and team or project management.

Develop global engagement strategies for Crunchyroll’s streaming platform using consumer behavior, audience segmentation, experimentation, and analytics. Analyze viewing journeys, engagement barriers, churn signals, and lifecycle interventions. Build measurement frameworks, define success metrics, evaluate A/B tests, and identify strategies that strengthen viewing habits and retention. Partner with Product, Data Science, Lifecycle, Curation, and Consumer Insights teams to translate complex behavioral data into executive-level recommendations and prioritized initiatives.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Designs, builds, and maintains scalable data pipelines, microservices, and cloud-based data systems. Develops real-time and batch processing solutions using Python, Java, and AWS services; creates data integrations and workflows; translates requirements into system designs; implements testing and validation; and monitors distributed pipelines for reliability, performance, and data quality.