Sr. Director, Data & AI Engineering - SLC Management

Opportunity to shape SLC Management’s enterprise data, analytics, and AI strategy and platform roadmap.

High visibility role with significant impact across investment, distribution, and partner functions.

A performance-driven, inclusive culture grounded in core values: client obsessed, value each other, act with speed, and an owner’s mindset.

Define and communicate a compelling multi-year vision for data, integration, AI engineering, and platform capabilities aligned to firm objectives.

Own the platform roadmap (prioritization, resourcing/squads, budget, dependency management, and measurable outcomes).

Partner with Architecture, Security, Risk, and Business leaders to align priorities, ensure adoption, and drive change management across the organization.

Champion modern delivery methodologies (agile, data products, data mesh) to increase speed-to-value and business impact.

Provide executive and hands-on technical leadership for scalable data pipelines supporting batch, micro-batch, and streaming workloads.

Lead enterprise data lake architectures for structured and unstructured data ingestion, curation, metadata, and lifecycle management.

Oversee Snowflake data warehouse environments including data modeling, performance tuning, workload management, security controls, and cost governance.

Establish standards for data reliability: automated data quality rules, lineage, observability, and operational SLAs/SLIs.

Drive the creation of reusable, governed data assets and data products to accelerate analytics and AI use cases.

Lead modernization of the Informatica IICS/IDMC ecosystem—including ingestion patterns, ETL/ELT designs, API-led integration, and automation.

Strengthen enterprise Data Quality practices (profiling, rules, exception workflows) and integrate DQ into CI/CD and operational monitoring.

Improve metadata automation, lineage capture, and governance integration across Informatica and Snowflake.

Lead engineering direction for AI solutions, including LLM-based capabilities, retrieval-augmented generation (RAG), vector search, and evaluation frameworks.

Establish production-grade AI engineering patterns (prompt management, guardrails, model monitoring, and human-in-the-loop workflows).

Partner with Risk, Compliance, and Security teams to implement Responsible AI controls and governance that enable innovation while managing risk.

Provide leadership for cloud infrastructure and platform operations supporting data and AI services on AWS (networking, compute, storage, security, IAM).

Establish and mature SRE/operations practices: incident management, problem management, runbooks, on-call readiness, and post-incident learning.

Own platform resilience and continuity planning, including backup, disaster recovery, failover testing, and capacity management.

Implement robust observability across pipelines and platforms (logging, metrics, traces) to ensure availability, performance, and cost transparency.

Ensure platforms meet enterprise security, privacy, and regulatory requirements through strong control design and continuous compliance.

Champion developer productivity by standardizing CI/CD, environment automation, code quality practices, and reusable templates/accelerators.

Lead Communities of Practice, internal technical forums, and architecture reviews—raising the technical bar and sharing best practices.

Create reference implementations…