We are seeking a Senior Data Engineer to design, build, and support scalable data pipelines and analytics datasets that power enterprise reporting across Finance, Technology, and Operations.
This role focuses on ingesting data from corporate systems, organizing it in a cloud-based data lake, and enabling reliable reporting through Amazon QuickSight.
The ideal candidate is a hands-on engineer comfortable with modern AWS data services, collaborating with business stakeholders, and supporting production reporting workloads. You will help establish practical standards for data ingestion, transformation, and reporting in a growing analytics environment.
Context: You will build and maintain Python-based ETL pipelines within AWS Glue, own transformation logic, manage orchestration and metadata cataloging, and ensure data is securely processed and accessible for downstream analytics and AI tools.
This role requires strong ownership of pipeline reliability, data quality, and performance tuning. Experience with dbt and Snowflake will become increasingly important as the platform evolves.
Key Responsibilities
Design, build, and maintain scalable data ingestion frameworks using AWS native services (Glue, Lambda, S3, Step Functions) and SnapLogic
Architect and manage the enterprise data lake on S3 using Apache Iceberg, including partitioning strategies, schema evolution, metadata optimization, and lifecycle management
Develop robust ETL/ELT pipelines to standardize, cleanse, and enrich source system data for analytics and operational use cases
Build and maintain reporting-ready datasets and queries using Amazon Athena and AWS Glue metadata
Implement and monitor data quality frameworks, including validation rules, reconciliation checks, and anomaly detection
Collaborate with Finance, Technology, and Operations stakeholders to translate business requirements into scalable data solutions
Establish and enforce data governance best practices: documentation, lineage tracking, access controls, and change management
Monitor pipeline health and performance, troubleshoot data issues, and support recurring reporting cycles
Performance Standards
Deliver projects accurately and on schedule
Maintain professionalism and accountability
Demonstrate effective teamwork and cross-functional collaboration
Align with Technology & Operations strategic goals
Communicate clearly with internal and external stakeholders
Ideal Candidate Profile
14+ years of overall software development experience, with significant exposure to data-centric systems
7+ years of hands-on experience in data engineering, analytics engineering, or related roles
Strong SQL skills with proven experience designing and building analytical datasets
Hands-on experience with AWS data platforms: S3, Glue, Athena, and AWS Unified Data Catalog
Experience integrating data from SaaS and enterprise systems using ETL/ELT tools such as SnapLogic
Experience supporting BI tools like Amazon QuickSight, Tableau, or Power BI
Ability to work independently and collaborate effectively with both technical and non-technical stakeholders
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