Description

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

Key Skills

Education

Any Graduate