The Technical Project Manager will own cloud data engagements from planning to execution, ensuring projects are delivered on time and within scope. They will lead cross-functional teams of data engineers and scientists while managing project documentation, risks, and stakeholder reporting.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. You will also mentor domain teams on data modeling and engineering best practices to improve platform templates.
Design and build agentic systems using frontier LLMs and develop document understanding pipelines for complex legal contracts. Collaborate with legal and commercial stakeholders to translate requirements into system logic and build evaluation sets to measure accuracy.
Build and maintain interactive dashboards and reports in Power BI to support business decision-making. Develop and optimize data models and transformations using Databricks and SQL while collaborating with stakeholders.
Define and maintain data architecture guidelines while acting as a technical liaison between business needs and engineering teams. Manage technical debt and identify opportunities to evolve service offerings into technical-commercial proposals.
Provide technical leadership and architectural guidance to the platform team while influencing decisions through technical credibility. Collaborate with stakeholders to modernize systems and implement scalable data infrastructure using modern backend and data stack technologies.
Design and build responsive, high-quality user interfaces for data products while collaborating with cross-functional teams. Take ownership of UX/UI decisions and integrate front-end components with back-end services and APIs.
Design, develop, and maintain backend microservices while owning the full lifecycle from implementation to deployment. Collaborate with cross-functional teams to define technical solutions and ensure system scalability and performance.
Partner with product managers and engineers to measure product performance and deliver actionable insights throughout the product lifecycle. Develop analytics frameworks for A/B testing and present data-driven recommendations to executive stakeholders to inform the product roadmap.
The role involves co-developing batch and streaming pipelines, semantic models, and transformations using Databricks. Additionally, the engineer will define data contracts, mentor domain teams on data modeling, and improve platform tooling based on feedback.
Define and maintain data architecture guidelines for ingestion, transformation, and governance while managing technical debt. Act as a technical liaison to translate business needs into technical solutions and commercial proposals.
Drive sales opportunities and manage the pipeline through RFPs, proposals, and networking activities. Build and maintain strong relationships with current clients to identify new growth opportunities and ensure satisfaction.
Design and scale agent-based AI systems and automation workflows for a major banking institution. Lead the end-to-end process of connector requests, from requirement gathering and security homologation to production enablement.
The Data Engineer Lead will co-develop batch and streaming pipelines while defining data contracts and quality rules. They are also responsible for mentoring domain engineers and guiding teams on data modeling and product ownership.
The role involves co-developing batch and streaming data pipelines on Databricks while defining data contracts and quality rules. Additionally, the engineer will mentor domain teams on data modeling and best practices to improve platform templates and tooling.
