• Build, maintain, and optimize data pipelines using Spark, Kafka, Airflow, and Python • Orchestrate workflows across GCP (GCS, BigQuery, Composer) and AWS-based systems • Model data using dbt, with an emphasis on quality, reuse, and documentation • Ingest, clean, and normalize data from third-party sources such as Google Ads, Meta, Taboola, Outbrain, and Google Analytics • Write high-performance SQL and support analytics and reporting teams in self-serve data access • Monitor and improve data quality, lineage, and governance across critical workflows • Collaborate with engineers, analysts, and business partners across the US, UK, and India

• Manage and oversee data migrations within the client onboarding process, gathering, analyzing, and processing client historical data during each migration, and following up with the client to ensure onboarding timelines are met • Provide customer training to ensure they understand the way in which they would interact with the system to achieve their goals • Support the creation of onboarding support assets for current and new customers • Create Excel import templates based on provided historical investment data • Build out Macros and automation to simplify the data import process to create efficiency and support continuous improvement efforts • Meet with the Product team to provide feedback on potential new features in development gathered from customer interactions • Participate in Customer Experience departmental meetings to share project priorities and communicate any roadblocks to the team • Join sales demos to help scope and set expectations for extensive customer data migrations as needed • Conduct quality checks on the conversion of sponsor and investor data imported into InvestNext’s Platform • Track KPIs and metrics to gauge the overall health of new onboarding customers

• Drive Data Architecture: Lead the implementation of scalable, fault-tolerant data infrastructure that powers our advanced data platform. • Lead Technical Strategy: Partner with senior colleagues to define and execute a cohesive data strategy that aligns technical solutions with company objectives. • Mentorship & Code Quality: Mentor mid-level and junior engineers, conduct code reviews, and enforce best practices for Python, SQL, and data modeling. • Evaluate & Innovate: Proactively identify, evaluate, and integrate new technologies (such as SQL Mesh) to future-proof our stack. • Cloud Native Workflow: Establish and maintain CI/CD pipelines, branching strategies, and project workflows using Azure DevOps and Jira. • Reliability & Governance: Champion data quality, security, and governance standards and ensure robust monitoring and alerting are in place. • Documentation: Own the technical documentation strategy, ensuring high-level architecture diagrams and runbooks are kept current. • Perform other duties as assigned.

• Design and implement cloud-native data pipelines and storage architectures supporting large-scale analytics workloads. • Develop and maintain infrastructure-as-code solutions using Terraform, Python, and CI/CD tools for automated provisioning and deployment. • Architect scalable, resilient cloud data environments leveraging AWS, Azure, or GCP. • Optimize data ingestion, transformation, and distribution frameworks for performance, security, and cost efficiency. • Collaborate with DevSecOps, data science, and software teams to align platform engineering with business and mission objectives. • Integrate and manage containerized workloads using Kubernetes, Docker, and related orchestration tools. • Support monitoring, logging, and observability pipelines for system reliability and uptime. • Ensure compliance with FedRAMP, NIST, and FISMA frameworks for secure federal cloud environments.