Senior Manager - Tech Development

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About the role

Position Summary The Tech Development Lead is a Grade 7 individual contributor who independently designs, builds, deploys, and operates reliable data pipelines and AI-enabled solutions. The role applies advanced technical judgment across data engineering, GenAI, and agent engineering, with accountability for production reliability, observability, trusted data access, prompt effectiveness, and responsible operation in a regulated environment. The role owns complex components of the data and AI landscape end-to-end, makes day-to-day technical decisions with limited oversight, and helps shape practical engineering direction. It partners with business, product, platform, architecture, security, governance, and engineering teams to translate ambiguous needs and user feedback into scalable, maintainable solutions while mentoring colleagues and strengthening team capability. Why This Role Exists • Own data pipelines, datasets, and supporting interfaces end-to-end for analytics, machine learning, and GenAI use cases. • Build and maintain AI agents and LLM-powered capabilities that support trusted, efficient business workflows. • Establish agent reliability, observability, guardrails, and escalation controls for stable and compliant production operation. • Use AI-assisted engineering as a core working practice while applying strong judgment to generated code, tests, documentation, recommendations, and downstream risk implications. Key Responsibilities 1. Data Pipeline & Platform Engineering • Independently design, build, deploy, and operate batch and streaming data pipelines that meet defined expectations for freshness, correctness, reliability, performance, and cost. • Own data models and datasets across ingestion, transformation, storage, and serving, using modern warehouse or Lakehouse technologies. • Monitor source and platform changes, including ServiceNow data exposed through Databricks views, and address inconsistencies that could affect downstream solutions or agent reliability. • Make informed technical decisions on pipeline, interface, data-model, and automation changes, escalating only where architectural, compliance, or business-risk thresholds require broader approval. • Control and monitor approved data access points across structured and unstructured sources, cloud platforms, documentation, and authoritative business repositories. 2. AI Agent & GenAI Engineering • Build, deploy, and enhance machine learning and GenAI solutions aligned with validated user requirements. • Develop and maintain AI agents using Python, Azure services, APIs, LLM frameworks, retrieval patterns, and appropriate interface technologies. • Use AI coding assistants and LLM tooling to plan, scaffold, refactor, test, document, and debug code, while validating outputs before adoption. • Create agent-based automations for data-quality investigation, failure triage, documentation, schema and lineage analysis, dataset discovery, and routine remediation. • Test, refine, and govern prompts; identify new prompts aligned with evolving business needs and approved solution scope. • Incorporate user feedback, agent metrics, and production outcomes into a closed-loop improvement process for prompts, models, and agent behavior. 3. Reliability, Observability & Trust Controls • Monitor system health, trace logs, response quality, hallucination indicators, data drift, failures, and operational trends. • Define and maintain practical detection thresholds, alerts, dashboards, and escalation paths for production issues. • Instrument pipelines and agents, respond to incidents, perform root-cause analysis, and drive corrective actions for reliability, quality, security, performance, and cost. • Validate that agent responses rely on approved authorita

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