Top Data & Analytics Jobs in Dallas, TX

The Sterling Data Analyst will enhance order entry systems, maintain Oracle databases, and analyze data for integration with Sterling applications, requiring strong SQL skills.

Own and maintain manufacturing master data in JDE, including item setup, BOMs and routings. Ensure data integrity across JDE and connected systems (EBT, palletizers, labelers, Lisam), enforce governance and compliance (FDA, ISO, SOX), support MOC/product transitions, and partner with Operations, Supply Chain, Quality, Engineering, and IT for ERP enhancements and data migrations.

Support LP Analyst's business strategy and client solutions by engaging in marketing efforts, conducting relationship development, and assisting in data analytics tasks.

Analyzes multi-channel marketing performance, conversion funnels, landing pages, and A/B tests to identify trends, performance drivers, and optimization opportunities. Builds recurring reports and executive-ready insights using large datasets, SQL, and advanced Excel. Partners with marketing, analytics, data, and operations teams to guide strategic decisions, improve data quality, and connect marketing metrics to revenue, unit economics, and company KPIs.

Analyzes global data center and digital infrastructure markets to support business planning, market entry, expansion, site selection, and executive decisions. Evaluates supply, demand, pricing, competition, land, power, infrastructure, and regulatory conditions; maintains market intelligence datasets and analytical frameworks; creates dashboards and executive presentations; and provides informal leadership and mentorship to junior colleagues.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

Build predictive data pipelines, machine learning models, dashboards, and automated decision tools for portfolio strategy. Analyze product performance, costs, monetization, competitive intelligence, customer behavior, and feature opportunities. Govern metrics and data quality using SQL while enabling self-service AI and natural-language data access. Support roadmap prioritization, ROI assessment, initiative tracking, and executive decision-making through advanced analytics and financial modeling.

Build and own Snowflake data models and dbt pipelines for a Customer Data Platform supporting marketing segmentation and customer journeys. Integrate complex source systems, manage data architecture and governance, conduct data discovery, and translate business rules into scalable solutions. Collaborate with engineering, analytics, product, and business stakeholders; lead technical workshops; mentor junior engineers; and implement automated data quality testing, CI/CD workflows, and AI-augmented engineering practices.

Analyze large and complex datasets using SQL, Python, Spark, Spark SQL, and PySpark in Microsoft Azure environments. Responsibilities include data transformation, validation, profiling, reconciliation, quality analysis, root-cause investigation, scalable data process improvement, documentation, and communicating findings to technical and business stakeholders.

Analyze member interactions, safety vetting cases, review decisions, and appeals for quality and policy compliance. Audit Trust and Safety workflows, identify process gaps, track departmental quality, report trends, present data-driven insights, and recommend improvements to processes and training. The role requires strong analytical, communication, confidentiality, attention to detail, and time-management skills.