Every asset-management skill we track.

Boxing your own workflow into stages an agent can execute under your supervision — the agentic-era skill that separates operators who stay in front of automation from those who get eaten by it.

Running an investment book of record for the front office next to an accounting book of record for the fund — and knowing which one answers which question.

The daily race to strike a fund's net asset value: pricing, accruals, fees, fair value and the administrator's file.

Turning a hypothesis into a tested, reproducible signal — data hygiene, survivorship, and honest backtests.

How an idea becomes an order, an order becomes an execution, and compliance sits in the middle of both.

Using Barra/Axioma-style models to see what a portfolio is really exposed to, and to budget risk deliberately.

Winning consultant ratings, answering DDQs and running the institutional sales cycle for a strategy.

Resolving breaks between custodian feeds, portfolio accounting, and CRM systems.

REST / SFTP / webhook patterns to read positions and write trades back into Orion, Tamarac, Addepar et al.

Extracting, transforming, validating, and loading historical investment data (positions, transactions, performance history) from legacy systems to target wealth platforms.

Model definitions, drift-band thresholds, cash-raise rebalances across thousands of accounts.

Building, prioritizing, and communicating a product roadmap for a wealth technology platform, balancing advisor needs, regulatory constraints, and technical feasibility.

Positions, securities, trades, models, accounts, households — the schemas every PMS exposes (or hides).

Writing and optimizing SQL queries against investment data warehouses covering positions, transactions, performance, client demographics, and fee data.

Running discovery, writing PRDs, and decomposing epics for back-office migrations.

Applying sprint-based delivery, backlog management, user story writing, and stakeholder ceremonies in regulated wealth and fintech environments.

pandas, numpy, scikit-learn applied to financial data engineering.

Evaluating when to configure a wealth SaaS platform within its standard feature set versus building custom extensions, with implications for upgrade risk and vendor support.

Designing the future-state organizational structure, processes, and technology interactions for a wealth management firm undergoing platform or business transformation.

Migrating off APL, Broadridge mainframes, and batch ledgers toward real-time platforms.

Designing and building extract-transform-load (or ELT) pipelines that ingest, clean, and deliver financial data from custodians, market data vendors, and operational systems.

Time- vs money-weighted return, GIPS composites, fee impact, account-level vs household.

Designing the go-live weekend cutover sequence, rollback decision criteria, and contingency playbooks for wealth platform migrations.

Drafting and managing professional services agreements, scope definitions, and change orders for wealth platform implementation and integration engagements.

Evaluating how platform migrations and new technology implementations affect compliance with SEC, FINRA, and state regulatory obligations.

Compliance-aware cloud architecture: KMS, audit trails, regional pinning.

Running RFPs, vendor scorecards, and total-cost-of-ownership models for platform decisions.

Designing weighted scorecards and scoring shortlists against client constraints.

Reviewing flagged events and deciding file vs escalate vs dismiss.

Industry data exchange standards governing how financial account, transaction, and market data flows between institutions, custodians, and technology platforms.

Building retrieval pipelines over 10-Ks, ADV filings, and internal compliance manuals.

Structured methodology for managing people-side change during wealth platform rollouts, including stakeholder analysis, resistance management, and adoption measurement.

Parsing, transforming, and mapping financial data payloads between vendor-specific schemas used by custodians, PMS platforms, and CRM systems.

Building transformation layers on Snowflake using dbt for custodial data standardization, performance metrics, and advisor-facing analytics pipelines.

Managing capital call schedules, distribution notices, NAV updates, and performance reporting for private equity, hedge fund, and real assets allocations in wealth portfolios.

BlackRock's enterprise risk and portfolio-management platform adapted for wealth managers, enabling factor-based risk analysis, model management, and discretionary portfolio management at scale.

Implementing secure single sign-on and delegated authorization between wealth platform components using industry-standard identity protocols.

Designing asynchronous, event-driven systems where platform actions (trade execution, account opening) trigger downstream workflows in real time.

Composing LLM + retriever + validator + human-in-loop pipelines (n8n / LangGraph thinking).

Decomposing portfolio return relative to benchmark into allocation, selection, and interaction effects using Brinson model or factor-based attribution.

Using integration-platform-as-a-service tools to orchestrate data flows between siloed wealth management applications without point-to-point custom code.

Using the Databricks Lakehouse platform and Spark for large-scale custodial data ingestion, transformation, and AI/ML workloads in wealth management environments.

Applying ML/statistical models to portfolio risk, factor attribution, and prediction.

Reading and explaining Monte Carlo plan outputs — when the 80% success number lies.

Electronic messaging standard for real-time securities order communication between wealth managers, OMS platforms, and broker execution venues.

Using embeddings and vector search (Pinecone, Weaviate, Azure AI Search, pgvector) to build semantic retrieval systems over financial documents and client data.