Data Product and Engineering Consultant - Total Portfolio Analysis

1. Work Plan and Operating Cadence - Deliverable: Work plan and operating cadence. - Expected output / acceptance criteria: - Confirmed scope, timeline, stakeholders, decision points, risks, and escalation path. - Review of the current TPV dashboard plan and recommendation for an incremental delivery approach aligned with the data platform build-out. - Identification of key trade-offs, risks, and dependencies. - Weekly status updates covering progress, open decisions, blockers, and next steps. - Review and approval: Chief of Data. 2. TPV Semantic Layer and Data Dictionary - Deliverable: TPV semantic layer and data dictionary. - Expected output / acceptance criteria: - Agreed definitions for each TPV data point and metric. - Documentation of attributes, grain, formula, inputs, frequency, history, owner, lineage, and business context. - Definitions that are clear and actionable for both business users and engineers. - Review and approval: Chief of Data; TPA dashboard working group. 3. Source Assignment and Data POVs - Deliverable: Source assignment and Data POVs. - Expected output / acceptance criteria: - Each mapped data point, metric, and attribute linked to an authoritative source system, dataset, and field. - Documentation of competing sources, gaps, overlaps, conflicts, and data quality issues through Data POVs. - Recommended source precedence and documented decision rationale. - Review and approval: Chief of Data; source owners. 4. Technical Requirements and Data Model - Deliverable: Technical requirements and data model. - Expected output / acceptance criteria: - Backlog-ready requirements covering: - Data model and domain data product implications. - Data transformations. - Data refresh requirements. - Data quality checks. - Dependencies and implementation priorities. - SQL/Python prototype logic, where useful. - Review and approval: Chief of Data. 5. Dynamic TPV Dashboard Prototype - Deliverable: Dynamic TPV dashboard prototype. - Expected output / acceptance criteria: - Interactive prototype developed in Power BI or Sigma. - Use of source data from individual systems where available. - Validation of dashboard direction and user interactions. - Validation of drill-down paths. - Identification and prioritization of production build requirements. - Review and approval: Chief of Data; dashboard leadership. 6. Handover Pack and Knowledge Transfer - Deliverable: Handover pack and knowledge transfer. - Expected output / acceptance criteria: - Documentation of key deliverables and decisions. - Consolidated list of open decisions and risks. - Implementation backlog inputs. - Recommended next steps for the OIM team. - Completion of knowledge transfer sessions. - Review and approval: Chief of Data. Cross-Cutting Requirement - Review the current TPV dashboard plan, data inventory, and delivery assumptions. - Recommend an incremental delivery approach aligned with the data platform build-out. Identify: - Early release opportunities. - Dependencies and sequencing choices. - Key trade-offs. - Priority adjustments, where needed.

New York; Remote

6 months

This position is located in the Office of Investment Management (OIM) of the United Nations Joint Staff Pension Fund (UNJSPF). OIM is responsible for the investment of the assets of the Fund. OIM manages a $100+ billion multi-asset global portfolio, with about 85% actively managed in-house. OIM is starting its Total Portfolio Approach (TPA) journey as part of Project Blue Print 2030. TPA manages the Fund as one portfolio, judged against the Fund's overall goals, instead of as separate asset class portfolios. A Total Portfolio View (TPV) dashboard is expected to bring public and private markets, risk, performance, liquidity and cost into one place. A working group has defined the dashboard use cases and an initial inventory of about 220 proposed data points and metrics. The next step is to confirm what those items mean, where they should come from, how source conflicts should be resolved and which reusable data products are needed underneath TPV. The consultant reports directly to the Chief of Data, OIM, and works under the Chief of Data's overall guidance and oversight. The consultant is expected to operate independently, structure ambiguous requirements, lead stakeholder alignment, identify risks and escalate only material blockers or decisions requiring management direction. This consultancy is based in New York and in-office presence is preferred. Hybrid work may be considered. CONSULTANT PROFILE • Senior, autonomous consultant with direct experience in TPA, total fund, or total portfolio / multi-asset portfolio analytics. • Strong data product and semantic-layer capability: able to define metrics, attributes, grain, calculation logic, lineage, source precedence and decision rules. • Strong enough in data engineering to translate business requirements into technical requirements, SQL or Python prototypes, data models and delivery-backlog inputs. • Business-facing and facilitative: able to align Investment, Risk, Operations and Data stakeholders when the same metric has different interpretations across teams or systems. • Clear writer and structured thinker: able to produce Data POVs that compare source options, explain nuanced differences and support decisions. SCOPE OF WORK 1. Semantic layer and data product definition • Review and complete the existing TPV data inventory with the working group. • Define each TPV data point or metric with clear business meaning, attributes, grain, formula, inputs, frequency, history, owner, lineage and decision rules. • Identify reusable domain data product implications underneath TPV, including where common definitions, attributes or source data should support more than one dashboard or use case. 2. Source assignment and Data POVs • Assign each mapped definition to its authoritative source across systems, including but not limited to Northern Trust, Bloomberg, MSCI Barra One, Burgiss and internal registers, as applicable. • Identify gaps, overlaps, conflicts and data quality issues. For each source, dataset, field, metric or attribute proposed for TPV use, assess availability, authority, completeness, quality, timeliness, granularity, lineage, access feasibility, current-offering availability and business agreement. • Produce Data POVs, meaning short decision papers that compare source options for the same metric or attribute, including definition differences, calculation logic, timing, data quality, lineage, recommended source and decision rationale. 3. Technical requirements and delivery backlog inputs • Write technical requirements for the TPV dashboards and supporting semantic layer, including data model, transformations, source precedence, refresh needs, data quality checks and implementation dependencies. • Translate sourcing decisions and domain data product implications into prioritized inputs for the data platform delivery backlog. • Develop SQL or Python prototype logic where useful to validate data modelling assumptions or make requirements executable for the OIM engineering team. 4. Delivery sequencing and incremental implementation • Assess the current TPV dashboard plan, sequencing and delivery assumptions based on data availability, source readiness, semantic-layer complexity and implementation dependencies. • Recommend an incremental delivery approach that aligns TPV outputs with the data platform build-out, including what can be delivered early, what depends on platform capabilities, and which decisions or dependencies need to be resolved first. • Identify and document key trade-offs, including speed versus quality, prototype versus production readiness, available source versus authoritative source, short-term workaround versus reusable data product, and dashboard priority versus data platform dependency. 5. Dashboard prototype • Build or guide a mock-up of an active, dynamic TPV dashboard in Power BI or Sigma, using source data from individual systems where available. • Make the prototype interactive, with filters, changeable variables and drill-down paths rather than static charts. • Use the prototype as a proof point to validate semantic-layer decisions, dashboard direction and production build priorities. 6. Stakeholder facilitation and best practices • Facilitate working sessions, meetings and decision discussions across Investment, Risk, Operations, the TPA dashboard working group and the Business Intelligence Team. • Drive alignment where the same metric or attribute has different meanings across teams or systems. • Share practical approaches, lessons learned and best practices from comparable TPA, total fund or multi-asset portfolio analytics implementations. WORKING ARRANGEMENTS • The consultant joins the weekly TPA dashboard working group and gives a short written status update each week. • The consultant is expected to be available for regular on-site meetings, working sessions and stakeholder discussions in New York, as required. • The consultant must not share project details without written consent from UNJSPF, and must follow OIM data classification and information security rules. • The consultancy is administered by UNJSPF, and all relevant UNJSPF rules, policies and procedures apply.

Advanced university degree (master's or equivalent) in computer science, data science, engineering, finance or a related field. A first-level degree with additional qualifying experience may be accepted instead. Minimum 7 years of advanced data engineering, data modelling or data product delivery experience is required; 10 years or more is desirable. Direct experience with TPA, total fund, or total portfolio / multi-asset portfolio analytics, including total fund exposure, factor, risk, performance and liquidity views is required Experience with investment data at a pension fund, asset owner or large asset manager, covering public and private markets is required. Proven experience mapping data definitions to source systems and writing technical requirements is required. Experience translating business use cases into reusable data product requirements, source decisions and implementation priorities is required. Experience facilitating business and technical alignment across functions where definitions, source choices or implementation priorities require agreement is required Experience building or guiding interactive dashboard prototypes (Power BI and/or Sigma) from multiple sources is required. Ability to work across business lines and explain data topics clearly to non-technical senior stakeholders is required. SQL and Python; data modelling, semantic layer design, data warehouse and cloud data platform experience is required (Snowflake is an advantage). Power BI and/or Sigma is desirable. Familiarity with Bloomberg, MSCI Barra One, Burgiss, and custodian or accounting data is desirable.

Fluency in English is required.

Not available.