Lead Architect - Applications

Iberdrola•Boston, MA

1d•$128,320 - $167,600•Onsite

About The Position

The Lead Architect – Applications plays a key role in designing and implementing enterprise data solutions that support advanced analytics, cloud adoption, and digital transformation. This position focuses on SAP-centric ecosystems, cloud platforms, and real-time data integration, while contributing to enterprise data strategy and ensuring compliance with governance and security standards. The Lead Architect – Applications role reports into the Manager – Data Analytics.

Requirements

Bachelor’s degree in Computer Science or a related field and a minimum of 8 years of relevant experience. An equivalent combination of education and experience may be considered.
Strong experience with SAP HANA, BW, Datasphere, BTP, and SAC.
Proficiency in SQL and data engineering tools (SSIS, SSRS, SSDT).
Familiarity with Python and ML frameworks (NumPy, Pandas).
Experience with enterprise data architecture, governance, and visualization tools (Power BI, SAP Analytics Cloud).
Expertise in migrating between different data platforms.
Proven experience in implementing data platforms and products across large-scale cloud infrastructure including Azure and AWS.
Ability to work on complex projects and collaborate across IT and business teams.
Project experience: Led multiple enterprise-scale projects involving the integration of advanced analytics, AI/ML capabilities, and automation frameworks, ensuring robust architecture and operational excellence throughout the project lifecycle.
Proven experience in implementing data platforms and products across large-scale cloud infrastructures, including Azure and AWS.

Nice To Haves

Advanced certifications in SAP, cloud platforms, or data architecture.
Hands-on experience with cloud-native architecture (AWS/Azure), Databricks, Snowflake.
Exposure to event-driven systems, AI/ML platforms, and automation frameworks.
Demonstrated experience in the end-to-end deployment of data platforms and data products.

Responsibilities

Support the design and implementation of enterprise data warehousing solutions using SAP HANA, BW, SQL Server, and related technologies.
Develop and optimize HANA models, SQL scripting, and performance tuning for efficient data processing and reporting.
Develop secure, data-driven applications integrating APIs and databases for real-time business insights.
Translate business requirements into scalable technical solutions aligned with best practices.
Participate in project implementation phases, including architecture, solution design, and deployment.
Provide technical guidance to development teams and ensure adherence to data governance and visualization standards.
Collaborate with business stakeholders and vendors to deliver high-quality solutions.
Contribute to the architecture of cloud-native data platforms (AWS/Azure) and Lakehouse ecosystems (Synapse, Databricks, Snowflake, SAP Datasphere).
Assist in designing real-time, event-driven data architectures leveraging Kafka or event mesh for grid operations and loT integration.
Support integration of AI/ML pipelines and predictive models into enterprise platforms for advanced analytics.
Help implement data governance and quality frameworks, ensuring compliance and lineage tracking.
Participate in developing ESG and sustainability data frameworks, including carbon modeling and energy optimization pipelines.
Assist in automation initiatives for data engineering, including CI/CD for data pipelines and infrastructure-as-Code (IaC).
Ensure cybersecurity principles are applied in data architecture, including encryption for sensitive data flows.
Coordinate with vendor partners to ensure seamless end-to-end deployment of enterprise data solutions, including alignment of technical requirements, integration activities, and ongoing support throughout the project lifecycle.
Responsible for collaborating with IT teams to efficiently address and troubleshoot Data Platform Administration issues.
Automate workflows to simplify Platform management and minimize manual tasks.
Design, develop, and automate AI-driven workflows within the enterprise data platform, focusing on seamless integration of machine learning models and efficient deployment of AI solutions. This includes leveraging the platform’s data pipelines and infrastructure to enable advanced analytics, ensure scalability, and maximize platform efficiency for business operations.