BI & Data Engineer

What You Will Build

Data Lake Architecture

• Design and implement modern data lakes on AWS and/or Azure
• Ingest raw data from ERP, CRM, and transactional systems
• Define data cataloguing, partitioning strategies, governance controls, and lifecycle management
• Ensure scalable, auditable, and cost-efficient storage aligned with enterprise standards

MongoDB Data Engineering

• Own and manage production MongoDB environments
• Design schemas optimized for analytics and high-volume workloads
• Build and optimize aggregation pipelines and indexing strategies
• Manage Atlas clusters, performance tuning, and Atlas Search integration

AWS Data Services

• Develop ETL and ELT pipelines using AWS Glue
• Query large-scale datasets with Athena
• Build streaming and real-time data flows using Kinesis
• Deliver analytics-ready data products on S3-backed data lakes
• Work with additional AWS services such as DynamoDB or EMR as needed

Power BI Development

• Build enterprise-grade Power BI solutions used by Finance, Operations, and leadership
• Design star-schema data models for performance and clarity
• Create advanced DAX measures and calculations
• Implement Row-Level Security (RLS) and incremental refresh
• Automate refresh from live operational sources

BI Reporting & Analytics

• Translate business questions and KPIs into robust analytical models
• Design intuitive and executive-ready dashboard experiences
• Deliver near real-time reporting for operational and management decision-making

What We're Looking For

Core Requirements

• 5+ years of data engineering experience building production ETL/ELT pipelines for enterprise or external clients
• MongoDB ownership experience - schema design, aggregation pipelines, cluster management, and performance tuning (not just consumption)
• Strong experience with AWS data services: Glue, Athena, S3, plus at least one of Kinesis, DynamoDB, or EMR
• Deep expertise in Power BI, including:
  • Advanced DAX
  • Enterprise data model design
  • Row-level security
  • Automated and incremental refresh
• Advanced SQL skills, including complex joins, window functions, and query optimization
• Python for data transformation and pipeline logic (Pandas, PySpark, and/or dbt)
• Solid understanding of data governance, including:
  • Data lineage
  • Data quality controls
  • Documentation to support audit and compliance requirements

Nice to Have

• AWS Data Analytics - Specialty certification
• Microsoft Power BI Data Analyst Associate certification
• MongoDB Associate Developer certification
• Experience with Azure Synapse Analytics or Azure Data Factory
• Experience with Apache Spark or Databricks for large-scale transformations
• Hands-on use of dbt (data build tool) for modular, tested transformations
• Prior experience delivering BI solutions for Insurance, FinTech, or Financial Services clients