• Design, develop, and maintain scalable ETL (Extract, Transform, Load) pipelines to process large volumes of data from diverse sources. • Build and optimize data storage solutions, such as data lakes and data warehouses, to ensure efficient data retrieval and processing. • Integrate structured and unstructured data from various internal and external systems to create a unified view for analysis. • Ensure data accuracy, consistency, and completeness through rigorous validation, cleansing, and transformation processes. • Maintain comprehensive documentation for data processes, tools, and systems while promoting best practices for efficient workflows. • Collaborate with product managers, and other stakeholders to gather requirements and translate them into technical solutions. • Participate in requirement analysis sessions to understand business needs and user requirements. • Provide technical insights and recommendations during the requirements-gathering process. • Participate in Agile development processes, including sprint planning, daily stand-ups, and sprint reviews. • Work closely with Agile teams to deliver software solutions on time and within scope. • Adapt to changing priorities and requirements in a fast-paced Agile environment. • Conduct thorough testing and debugging to ensure the reliability, security, and performance of applications. • Write unit tests and validate the functionality of developed features and individual elements. • Writing integration tests to ensure different elements within a given application function as intended and meet desired requirements. • Identify and resolve software defects, code smells, and performance bottlenecks. • Stay updated with the latest technologies and trends in full-stack development. • Propose innovative solutions to improve the performance, security, scalability, and maintainability of applications. • Continuously seek opportunities to optimize and refactor existing codebase for better efficiency. • Stay up-to-date with cloud platforms such as AWS, Azure, or Google Cloud Platform. • Collaborate effectively with cross-functional teams, including testers, and product managers. • Foster a collaborative and inclusive work environment where ideas are shared and valued.

• 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.