• Design, build, and maintain scalable AWS data platforms supporting batch and streaming pipelines, analytics, and AI/ML workloads, aligned with AWS Well-Architected best practices. • Build and operate data ingestion, transformation, and enrichment pipelines from internal systems and external APIs, handling structured, semi-structured, unstructured, and graph data. • Implement data normalization workflows to ensure consistent schemas, high data quality, and reliable analytics, BI, and ML use cases. • Design and enforce data governance including cataloging, lineage, access control, and auditability. • Build and maintain knowledge graphs to model relationships across core business entities, enabling advanced analytics and inference. • Identify data gaps, inconsistencies, and missing relationships using strong analytical and inference skills. • Integrate data from enterprise platforms such as CRM and ERP systems (Salesforce, HubSpot, SAP, NetSuite, Dynamics 365, Workday). • Design secure data access layers for analytics, BI, ML, and downstream applications. • Implement monitoring, observability, and data quality checks for freshness, completeness, and pipeline health. • Optimize data architectures for performance and cost efficiency using partitioning, indexing, compression, and storage tiering. • Build internal tooling, dashboards, and standardized scaffolding to improve visibility, maintainability, and onboarding. • Collaborate with cross-functional teams to deliver high-impact data solutions and share best practices, documentation, and technical guidance.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves leading a dynamic data engineering team dedicated to developing innovative solutions grounded in AI technologies. - Lead the data engineering team and manage all associated activities. - Drive the development of data architecture leveraging AI innovations. - Implement data engineering templates and architectures for optimized performance. - Collaborate with product and architecture teams to enhance data strategies. - Stay updated on AI trends to enhance team performance and relevance. - Manage internal stakeholders and represent the organization to clients as needed. - Oversee team performance evaluations and professional development initiatives. - Define results and take action to meet organizational objectives. Qualifications - Bachelor’s degree or equivalent work experience. - 7+ years of experience in data engineering/IT/Project Management, including management experience. - 2+ years leading projects with proven project management methodologies. - Experience with data integration/ETL tools like databricks, Hadoop, and AWS tech stack. - Familiar with AI engineering concepts and tools. - Proficiency in software development and database management. - Excellent communication and interpersonal skills. Benefits - Competitive salary ranging from $130,000 to $165,000. - Discretionary bonus eligibility. - Comprehensive benefits package including medical, dental, and vision. - 401(k) savings plans. - Generous paid time off policies. - Flexible remote working conditions.

• Design, develop, and maintain robust, scalable data pipelines using Python and Airflow • Collaborate with Analytics Engineers and other team members to deliver clean, well-modeled data to downstream users • Implement monitoring, logging, and alerting to ensure data pipeline reliability and performance • Ensure data quality and integrity through validation frameworks and testing • Optimize performance and cost-efficiency of cloud-based data systems • Contribute to architectural decisions around data storage, workflow orchestration, and infrastructure as code • Document data pipelines and processes

• Build and maintain ETL pipelines that ingest daily Excel/CSV trade files, transform and validate the data, and persist it to PostgreSQL • Design and evolve database schemas using SQLAlchemy ORM and Alembic migrations • Implement business logic services: shareholder balance tracking, sharebook reconciliation, movement detection, classification rules • Maintain and extend Elasticsearch sync services (incremental and full resync workflows) • Support business stakeholders with data questions using the ELK stack — writing and running queries, interpreting results, and surfacing relevant data from Kibana • Parse and process structured financial data files (Excel, CSV) using Pandas, including data validation and deduplication • Write robust, type-safe Python following project standards (mypy strict mode, Black, isort) • Write and maintain automated tests with pytest — unit tests, repository mocks, snapshot tests • Collaborate with the CTO on data flow design and review migration strategies before deployment