Top Entry Level Data & Analytics Jobs in Austin, TX

Develops and maintains Power BI dashboards and data models, defines KPIs with stakeholders, validates data accuracy, and collaborates with Data Engineering on optimized analytical models. The role documents data lineage and reporting assumptions, supports self-service BI through communications and training, and automates manual reporting processes. It requires SQL, Power BI, DAX, Power Query, data modeling, and data warehouse knowledge, plus strong communication and cross-functional collaboration skills.

Supports the Mortgage Lending Division by producing and maintaining routine reports, dashboards, data extracts, and basic analyses. Validates data accuracy, automates reporting where appropriate, documents procedures, investigates trends and audit issues, and responds to operational reporting needs. The role involves learning internal systems, metrics, and business processes while collaborating with stakeholders. Candidates should have basic Excel and data visualization skills, foundational SQL knowledge, strong analytical abilities, and zero to two years of relevant experience.

Supports the Mortgage Lending Division by producing and maintaining routine reports, dashboards, data extracts, and ad-hoc analyses. Validates data accuracy, documents reporting procedures, investigates trends and audit issues, and collaborates with business teams to understand performance metrics and reporting needs. The role requires basic Excel and data visualization skills, foundational SQL knowledge, attention to detail, and a willingness to learn internal systems and processes.

Analyze and tune sanctions, fraud, and AML models; evaluate performance, reduce false positives, develop features and datasets, automate analysis, troubleshoot data issues, and communicate findings to technical, compliance, regulatory, and senior management stakeholders.

Lead the design and development of Azure-based big data solutions, including batch and real-time ETL/ELT pipelines, data architecture, ingestion, curation, warehousing, and storage. Build solutions using Azure Data Factory, Databricks, Synapse, Spark, Python, SQL, and related Azure services. Support Salesforce migration and integration while applying strong RDBMS, data management, and big data design expertise.

Build and maintain Google Cloud data pipelines and ETL/ELT processes supporting GenAI and RAG solutions. Classify and govern sensitive data using Dataplex and Cloud DLP, enforce access controls, assess AI readiness, and design reusable schemas and data models. Collaborate with engineering, delivery, and client teams to integrate legacy systems, monitor pipeline reliability, conduct data quality testing, document data flows, and proactively identify compliance and delivery risks.

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Designs, implements, and optimizes Salesforce Data Cloud solutions. Responsibilities include configuring data streams, integrating Salesforce and legacy data with MuleSoft or ETL tools, mapping data objects, creating identity resolution rules, building calculated insights with SQL, developing customer segments, configuring activation targets, and managing data governance, privacy compliance, system performance, storage, and usage.

Own data quality across systems by designing validation rules, reconciliation processes, profiling workflows, and quality controls. Investigate discrepancies to identify root causes, apply statistical methods to detect anomalies, evaluate machine learning model outputs, and communicate findings to technical and non-technical stakeholders. Partner with engineering, BI, and business teams to establish data quality standards, metrics, controls, and reporting practices.

Build and maintain full-stack data engineering solutions supporting BioIntelliSense’s remote patient monitoring and clinical intelligence platform. The role likely involves processing health-monitoring data, enabling analytics, and supporting reporting for clinicians, patients, and employers. Specific responsibilities and qualifications are not provided in the description.