Senior Associate

Ziphire.hr•New York, NY

About The Position

As an Enterprise Technologies Data Analytics Engineer, your primary responsibility will be to design, build, and maintain robust data integration solutions that power an enterprise data lake in AWS. This data lake solution will integrate data from multiple platforms across the organization and service as a foundational asset for technology cost transparency, operational analytics, and advanced AI-driven insights. The Data Integration Engineer will work closely with data, infrastructure/operations, and service management teams to ensure that data flows are accurate, reliable, secure, and aligned with organizational standards and strategic priorities. What You’ll Do: Design, develop, and maintain scalable data integration pipelines that ingest, transform, and curate data from multiple source systems into the AWS-based data lake. Implement and optimize batch and, where applicable, streaming data pipelines using modern AWS service and integration frameworks. Ensure that ingested data is structured in a way that supports both current and anticipated analytical and AI use cases. Establish and maintain data mappings and documentation to ensure traceability and transparency. Support ongoing operations of the data lake, including performance tuning, issue resolution, and continuous improvement of data integration processes. Collaborate with platform owners and subject matter experts to understand source data semantics, business rules, and required transformations. Partner with data scientists and AI engineers to provide high-quality, well-documented data sets for modeling and insight generation. Experience collaborating and operating in an agile or product-oriented environment.

Requirements

Advanced degree in Computer Science, Information Systems, Engineering, or a related quantitative field.
8+ years of experience applying data integration, data modeling and data transformation techniques to address real-world business and technology solutions.
Strong experience with AWS data and integration services (e.g., S3, Glue, Redshift, or other similar technologies).
Proficiency in Python and SQL, data integration and ETL/ELT development, and modern data pipeline orchestration technologies.
Hands-on experience integrating data from multiple, heterogeneous source systems (service management tools, infrastructure monitoring, application platforms, and financial systems) into a harmonized, well\-structured data environment.
In-depth understanding of building data models and curating data to support AI and advanced analytics, including trend analysis, pattern detection, and identification of automation opportunities.
Ability to translate analytical findings into clear business insights and collaborate effectively with cross-functional partners.
Experience in communicating and simplifying complex ideas, presenting impact, trade-offs, and recommendations to non-technical partners.
Demonstrated ownership mindset, with a focus on reliability, maintainability and scalability, and continuous improvement of data integration solutions.

Responsibilities

Design, develop, and maintain scalable data integration pipelines that ingest, transform, and curate data from multiple source systems into the AWS-based data lake.
Implement and optimize batch and, where applicable, streaming data pipelines using modern AWS service and integration frameworks.
Ensure that ingested data is structured in a way that supports both current and anticipated analytical and AI use cases.
Establish and maintain data mappings and documentation to ensure traceability and transparency.
Support ongoing operations of the data lake, including performance tuning, issue resolution, and continuous improvement of data integration processes.
Collaborate with platform owners and subject matter experts to understand source data semantics, business rules, and required transformations.
Partner with data scientists and AI engineers to provide high-quality, well-documented data sets for modeling and insight generation.
Experience collaborating and operating in an agile or product-oriented environment.