Senior Cloud Data Analyst & Analytics Platform

Leander, TX

LinkedIn Darius Campbell 872-***-****

*****************@*****.***

Data Analyst with hands-on experience designing scalable data ecosystems on Google Cloud Platform and leading enterprise-scale cloud migrations. Skilled in building self-serve analytics platforms, enforcing data governance and security frameworks, and driving automation with Infrastructure as Code. Trusted advisor to executive stakeholders, collaborating cross-functionally in Agile/DevOps environments to define strategic data roadmaps and deliver business-aligned insights. Excellent writing and documentation skills ensure clear, maintainable processes across teams. Focused on operational efficiency, cost optimization, and measurable business outcomes. Work Experience

Senior Data Analyst Texicare Feb 2022 – Aug 2026

- Designed and maintained enterprise Power BI dashboards using DAX and Power Query to monitor patient outcomes, claims trends, and operational KPIs across multiple healthcare programs and clinical initiatives.

- Built and optimized scalable SQL-based data models supporting standardized metrics across clinical, claims, and operational reporting systems, ensuring consistency in enterprise-wide reporting definitions.

- Developed automated reporting pipelines using Power BI, Power Query, and Excel, reducing manual reporting effort and improving timeliness of executive and operational insights.

- Performed advanced data extraction, transformation, and validation using SQL and Excel, ensuring high data quality across large-scale healthcare datasets including EHR, claims, and operational systems.

- Partnered with clinical, finance, and operations stakeholders to translate business requirements into analytical solutions, defining KPIs and measurement frameworks aligned with organizational goals.

- Conducted in-depth patient journey and claims analysis using SQL and statistical techniques to identify trends in care delivery, utilization patterns, and healthcare outcomes.

- Implemented data quality frameworks and anomaly detection checks to identify inconsistencies across reporting datasets, improving reliability of dashboards used by leadership teams.

- Integrated and reconciled data from multiple healthcare systems including EHR, claims, and operational databases to create unified reporting views and a single source of truth.

- Developed ad hoc analytical solutions using SQL, Excel, and Python to support leadership decision-making, delivering actionable insights under tight timelines.

- Created and maintained documentation for data models, dashboards, and reporting logic to ensure transparency, governance, and long-term maintainability of analytics assets.

- Applied statistical analysis techniques using Excel and Python to support forecasting, performance tracking, and evaluation of healthcare program effectiveness.

- Identified operational inefficiencies through data profiling and analysis, recommending process improvements that enhanced reporting accuracy and workflow efficiency.

- Supported BI tool adoption and training (Power BI), enabling cross-functional teams to interpret data and independently leverage dashboards for decision-making.

- Monitored dashboard performance and data pipelines using validation rules and quality checks, ensuring accuracy and consistency of executive-level reporting systems.

- Led migration of on-premise reporting infrastructure to GCP BigQuery, implementing data partitioning, clustering, and query optimization to reduce cost and improve query performance.

- Defined data governance policies including IAM roles, VPC Service Controls, and data masking for sensitive patient information, ensuring compliance with healthcare regulations.

- Collaborated with engineering teams to deploy Terraform-managed Dataflow pipelines for real-time ingestion of streaming claims data, enabling near-real-time analytics.

- Architected self-serve analytics layer using Looker and BigQuery, allowing business users to explore data independently while enforcing governance constraints.

- Developed automated data quality checks using Great Expectations and Dataplex, reducing manual validation effort and improving data reliability across reporting systems. Data Analyst HCL Technologies May 2019 – Dec 2021

- Engineered real-time ETL pipelines using Google Cloud Platform (Dataflow, Pub/Sub, BigQuery) and Apache Beam to process customer, order, and inventory data, enabling near real-time analytics for retail operations.

- Built and optimized batch and streaming data pipelines using Python and Apache Kafka to ingest and process high-volume transactional and behavioral e-commerce data.

- Designed and maintained scalable data models supporting structured and semi-structured datasets, enabling reliable reporting, forecasting, and advanced analytics across business teams.

- Developed data transformation workflows in Python and SQL to clean, standardize, and aggregate large-scale datasets for BI dashboards and machine learning use cases.

- Integrated multiple data sources into a unified cloud data lake using Google Cloud Storage and BigQuery, improving data accessibility and cross-team analytics capabilities.

- Implemented data partitioning, clustering, and query optimization techniques in BigQuery, significantly improving dashboard performance and reducing query latency.

- Collaborated with marketing and product teams to build customer segmentation and campaign analytics models, improving targeting accuracy and marketing ROI.

- Supported development of recommendation and personalization data pipelines using customer behavior and clickstream data to enhance user engagement and conversion rates.

- Built and maintained automated data quality frameworks with validation rules and anomaly detection to ensure consistency and reliability of reporting datasets.

- Developed real-time data pipelines for customer service and operational analytics, integrating interaction data to improve call center and support efficiency.

- Integrated BI tools such as Tableau and Power BI with BigQuery to deliver executive dashboards for KPIs including sales performance, funnel conversion, and customer retention.

- Automated deployment and orchestration of data workflows using Jenkins and Google Cloud Functions, improving reliability and reducing manual intervention in production pipelines.

- Implemented data governance controls using IAM and VPC Service Controls to secure sensitive customer data in GCP environments.

- Designed and deployed Dataproc clusters for large-scale batch processing of historical sales data using PySpark and Hadoop ecosystem tools.

- Created infrastructure-as-code modules using Terraform to provision GCP resources (BigQuery datasets, Cloud Storage buckets, Dataflow jobs) ensuring repeatable deployments.

- Developed documentation and runbooks for data pipelines, enabling smooth handoffs and operational continuity across data engineering teams.

- Assisted in migrating legacy on-premise data warehouses to BigQuery, performing schema mapping, data validation, and performance tuning.

- Built real-time streaming dashboards using Looker connected to Pub/Sub and BigQuery, providing immediate visibility into operational metrics.

Data Analyst Gravity Systems May 2013 – Apr 2019

- Designed and built ETL data pipelines for processing business and operational data, enabling real time analytics and insights for improving organizational performance.

- Integrated multiple data sources including business applications, logs, and customer systems into centralized platforms for better reporting and decision support.

- Built and maintained data storage solutions using Azure Blob Storage, SQL Server, and Snowflake to support scalable data management across enterprise systems.

- Collaborated with internal teams to ensure high quality data for analytics and reporting, supporting accurate insights and business decision making.

- Integrated external systems with internal platforms, automating data ingestion to support reporting dashboards and operational workflows.

- Contributed to development of data driven applications, working with engineering and product teams to improve operational efficiency and user experience.

- Developed real time analytics solutions to monitor system performance and business operations, enabling faster decision making.

- Worked with cross functional teams to align data solutions with business requirements, improving efficiency across multiple business units.

- Supported analytics initiatives by providing expertise in data transformation and quality, helping teams generate meaningful insights.

- Contributed to design of dashboards and visualization tools using Tableau and Power BI, enabling teams to better understand data trends and performance.

Technologies and Languages

- Cloud Platforms & Data Stack: GCP (BigQuery, BigLake, Omni, Cloud Storage, Dataflow, Apache Beam, Dataproc, Cloud Composer, Pub/Sub, Confluent/Kafka, Looker, Vertx AI, BigQuery ML, IAM, VPC Service Controls, Data Masking, Encrytion, Data plex)

- Infrastructure & Automation: Terraform, Pululmi, GKE/Kubernetes, containerization, Infrastructure as Code, Jenksins, Gitub Actions, Docker

- Modern Data Stack: dbt, Airflow, containerization

- Programming & Scripting: Python, R, SQL, Scala, Bash

- Data Engineering & Processing: ETL/ELT pipeline development, Apache Spark (PySpark), distributed data processing, batch & streaming architectures, data integration, data modeling (star/snowflake schemas, 3NF)

- Data Warehousing & Databases: Snowflake, Amazon Redshift, BigQuery, PostgreSQL, MySQL, SQL Server, Azure Blob Storage

- Data Quality & Observability: Great Expectations, data validation frameworks, reconciliation pipelines, SLA monitoring

- Methodologies: Agile, DevOps, version-controlled infrastructure, automated governance Education and Certifications

- M.Sc. Information Technology, University of Arkansas Grantham, Little Rock AR 04/01/2015 – 11/05/2019

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