Senior Business Intelligence Engineer

Our Fortune 500 client (One of the world's companies) in Redmond, WA is looking for hardworking, motivated talent to join their innovative team. Are you a Senior Business Intelligence Engineer with a passion for collaborating with multiple teams and an interest in working onsite? Don't wait... apply today!

Position Title:

Senior Business Intelligence Engineer Location:

Onsite -

Redmond, WA Duration:

7+ months with high possibilities of extension

Pay Range:

$65 -70/hr on W2

Description:

We are seeking a highly analytical and data-driven Senior Business Intelligence Engineer to design and scale data solutions that drive strategic decision-making across the organization. This role requires deep expertise in large-scale data processing, cloud-based architectures, and advanced analytics to unlock insights from complex, high-volume datasets. The ideal candidate has a strong background in AWS data ecosystems, ETL development, data modeling, and business-facing analytics, with demonstrated experience influencing executive leadership. Key Responsibilities Data Engineering & Architecture Design, build, and maintain scalable ETL pipelines processing billions of records using modern data architectures (e.g., Apache Iceberg, S3, distributed query engines). Develop and optimize data workflows leveraging AWS services such as Redshift, Athena, Glue, Lambda, and EMR. Build and maintain data lakes and datamarts that consolidate data from disparate sources (S3, relational databases, logging systems). Ensure high data availability and performance for large-scale analytics workloads. Analytics & Business Intelligence Develop and maintain executive-facing dashboards and reporting solutions (e.g., QuickSight, Tableau, Power BI) to support weekly and quarterly business reviews. Translate complex business problems into data-driven insights, including opportunity tracking, revenue optimization, and operational efficiency metrics. Design multidimensional reporting frameworks to enable trend analysis and long-term strategic planning. Automate reporting processes to reduce manual effort and improve data accessibility across teams. Advanced Analytics & Data Science Apply statistical techniques (A/B testing, regression models, segmentation algorithms) to solve business problems and guide decision-making. Perform deep-dive root cause analysis on operational issues and system performance. Develop predictive or classification models to identify high-value segments and optimize targeting strategies. Data Quality & Governance Design and implement data validation frameworks to ensure accuracy, consistency, and reliability across datasets. Lead large-scale data migrations and ensure integrity across systems. Establish best practices for data governance, monitoring, and schema management. Cross-Functional Collaboration Partner closely with Product, Engineering, Finance, and Business teams to define KPIs and deliver actionable insights. Communicate findings and strategic recommendations to senior leadership (Director/VP level). Collaborate on product analytics initiatives to improve user behavior tracking and feature adoption measurement. Required Qualifications 5+ years of experience in Business Intelligence, Data Engineering, or Analytics roles Advanced SQL skills with experience working on large-scale datasets (billions of rows) Strong experience with AWS data stack (Redshift, S3, Athena, Glue, Lambda) Proficiency in Python (or similar) for data processing and automation Experience building ETL pipelines and managing full data lifecycle Strong experience with BI tools (QuickSight, Tableau, or Power BI) Experience with data modeling, data warehousing, and datamarts Proven ability to influence business decisions through data insights Preferred Qualifications Experience working with distributed data processing frameworks (Spark, Trino, EMR) Knowledge of modern table formats (Apache Iceberg, Delta Lake) Experience with Airflow or workflow orchestration tools Background in statistical modeling or machine learning techniques Experience working with executive stakeholders in fast-paced environments Key Success Metrics Reduction in data pipeline latency and improvement in refresh cycles Increased adoption of dashboards and self-service analytics tools Quantifiable business impact (e.g., revenue growth, cost savings, efficiency gains) Data quality improvements and reduction in reporting errors Scalability and reliability of data infrastructure Are you interested ? Please click apply button! If you are not available or if this is not the right role at the moment, please share the job description with your friends and let us know if any of them show interest.