Job Summary
ADAPT + EBIP Engineer
Serve as a technical lead for hybrid work model data solutions designing and implementing scalable platforms using cloud services Python big data technologies and advanced data modeling. Collaborate with cross functional teams to architect secure and reliable data products that support analytics innovation and business growth while ensuring quality and operational excellence.
Responsibilities
Lead end to end design of scalable data architectures that align with business objectives and enable reliable analytics for critical decision making.
Design comprehensive data models that optimize storage retrieval and performance while maintaining consistency integrity and long term maintainability.
Develop robust Python based data pipelines that automate ingestion transformation and validation of large and diverse data sources.
Implement big data processing solutions using Spark to handle high volume datasets and deliver timely insights for downstream consumers.
Architect cloud native data platforms by leveraging core services across GCP Azure and AWS to maximize resilience and cost efficiency.
Coordinate with product owners and analysts to translate analytical requirements into technical specifications and actionable data solutions.
Establish coding standards review practices and quality gates that improve reliability readability and maintainability of shared code assets.
Optimize data workflows for performance and scalability by tuning queries storage formats and partitioning strategies across large datasets.
Ensure data security and compliance by applying appropriate access controls encryption strategies and monitoring practices across environments.
Collaborate with infrastructure and operations teams to implement robust deployment monitoring and incident management for data services.
Guide peers on best practices in Python data modeling and big data technologies through code reviews knowledge sharing and mentoring activities.
Document data architectures data flows and design decisions in a clear and accessible format to support future enhancements and onboarding.
Engage with stakeholders to continuously improve data products by incorporating feedback measuring impact and iterating on delivered solutions.
Drive adoption of standardized data models that promote reuse interoperability and consistent metrics across business domains.
Align technical designs with sustainability and efficiency goals by selecting patterns and tools that reduce waste and improve resource utilization.
Qualifications
Showcase extensive hands on experience in Python programming applied to data engineering workflows and complex transformation logic.
Demonstrate strong expertise in conceptual logical and physical data modeling with a track record of implementing scalable data structures.
Bring proven experience as a data architect responsible for defining end to end data solutions across multiple systems and platforms.
Exhibit practical proficiency with core GCP services related to storage compute and data processing within production environments.
Display experience using Azure data and analytics services to design and operate enterprise data platforms in cloud settings.
Provide solid experience working with AWS data ecosystem components to deliver secure and reliable data solutions.
Apply deep understanding of Spark for distributed data processing performance tuning and optimization of large scale jobs.
Offer significant exposure to big data technologies and frameworks with experience handling structured and unstructured datasets.
Combine eight to ten years of overall industry experience in data engineering or architecture roles with increasing technical responsibility.
Communicate effectively in hybrid work environments and collaborate across locations using modern tooling and agile practices.
Certifications Required
Preferred certifications include Google Professional Data Engineer or Azure Data Engineer Associate or AWS Data Analytics Specialty or equivalent.
