Top Entry Level Analysis Reporting Jobs in Dallas, TX

Advise Technology stakeholders on data ownership, governance, risk, and management practices. Lead maturity assessments, strategic roadmaps, operating model improvements, metrics, executive reporting, and capability enhancement initiatives. Partner with senior leaders across Technology, Risk, Controls, and CDAO to improve data quality, metadata, regulatory alignment, and governance effectiveness. Identify opportunities to apply AI, automation, and modern data platforms to scale data management outcomes.

Participate in a rotational analyst development program focused on using data and technology to analyze performance trends, identify business opportunities, and support insurance operations. Complete training, mentorship, and rotational assignments while developing strategic, technical, analytical, communication, and business skills. Use tools such as Excel, SQL, Python, R, SAS, Snowflake, Power BI, and emerging AI technologies to generate business insights.

Analyze marketplace and product data using SQL and Snowflake, build Domo dashboards and reports, track metrics, evaluate experiments, identify trends, validate data quality, and provide recommendations to product, operations, and business stakeholders. The role also supports ad hoc analyses and analytics projects while documenting metric definitions and communicating findings to nontechnical audiences.

Supports onboarding and management of business units in the IntegrityCONNECT Commissions platform. Responsibilities include analyzing carrier and business-unit files, creating mapping guides, identifying contracting errors and exceptions, validating commission calculations, documenting system impacts, researching discrepancies, and collaborating with operations and contracting teams to resolve issues. The role develops expertise in commission processing, reporting, workflows, and insurance payment structures.

Develop and maintain quantitative models for volume forecasting, capacity planning, workforce scheduling, scenario analysis, vendor modeling, and operational reporting. The role monitors model performance, ensures Model Risk Management compliance, standardizes model usage, and presents findings to executive leadership. It requires advanced analytical, simulation, optimization, database querying, and quantitative problem-solving skills, with preferred experience in banking analytics and statistical modeling.

Analyzes business processes, operational metrics, financials, and system requirements to identify trends, resolve problems, and recommend improvements. Partners with management and cross-functional teams to implement process changes, ERP and forecasting solutions, Lean Manufacturing practices, training, and sustainable operational improvements affecting production, materials, and logistics.

Build and support scalable data platforms, pipelines, warehouses, lakes, integrations, and transformation processes. Improve data quality, observability, testing, monitoring, security, and platform performance. Collaborate with analysts, data scientists, engineers, product teams, and business stakeholders to deliver trusted data solutions. Opportunities may include cloud data engineering, architecture, streaming, analytics engineering, governance, and technical leadership. This is a proactive talent community posting for future opportunities, with travel up to 10%.

Supports the organization’s data governance framework by developing and enforcing standards, policies, business rules, and control frameworks. Monitors data quality and governance KPIs, analyzes root causes, leads improvement initiatives, maintains data definitions and documentation, supports audits, and promotes data stewardship across business and IT teams.

Designs, deploys, and optimizes production-grade data infrastructure, ETL/ELT pipelines, distributed databases, streaming systems, vector databases, APIs, and feature stores for AI and machine learning applications. The role also implements data quality, observability, lineage, testing, compliance, and secure cloud-native deployments. As a forward-deployed engineer, the employee partners directly with customers, prototypes solutions, transitions them to production, and feeds field learnings into platform improvements.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.