Forward Deployed Data Specialist

The Forward Deployed Data Lead acts as the single point of accountability and conduit between the business, product, data engineering, and data analytics functions for a dedicated business line (e. g., CFDs, cash equities, wealth). Operating at the intersection of business context and technical delivery, the role translates trading, wealth, and brokerage business needs into well-formed data requirements and then drives their end-to-end delivery as data products spanning data engineering, modelling, and analytics by orchestrating the extended data engineering and analytics team. The Forward Deployed Data Lead is expected to be a hands-on technologist as well as a trusted business partner, capable of sitting in a requirements conversation.

The core responsibilities for the job include the following:

Business and Requirements Partnership:

• Act as the dedicated data lead for a specific business line (CFD, cash equities, wealth, or similar), building deep functional knowledge of that line's products, trade lifecycle, client journeys, and regulatory/reporting needs.
• Serve as the primary conduit between business, product, data engineering, and data analytics teams, ensuring no requirement is lost in translation between commercial intent and technical build.
• Proactively engage business stakeholders (trading desks, RMs, product managers, and risk/compliance where relevant) to capture new and evolving data requirements as they arise.
• Formalize requirements into clear, structured artifacts (business requirement documents, user stories, data contracts, and source-to-target mappings) that both engineering and analytics teams can execute against.
• Prioritize and sequence requirements in partnership with Product and Business, balancing quick wins against long-term data platform integrity.

Delivery Ownership (Engineering + Analytics):

• Own end-to-end delivery of formalized requirements from data source identification and ingestion, through modelling and transformation, to analytics consumption (dashboards, self-serve datasets, AI-ready data marts).
• Design the target data model and pipeline architecture and work hands-on with data engineers to build/validate ingestion (batch and streaming) and transformation logic.
• Partner with data analysts to design analytics use cases, metrics definitions, and semantic layers that are accurate, reusable, and aligned to business language.
• Review code, data models, and dashboards produced by the extended team to ensure quality, performance, and consistency with platform standards.
• Manage delivery timelines, risks, and dependencies for the business line's data roadmap, escalating proactively where needed.

Governance, Coordination, and Cadence:

• Coordinate daily with the Head of Data Platform and Head of Data Analytics on design decisions, delivery status, and resourcing for the business line.
• Ensure designs align with the firm's broader data architecture, governance standards, data quality frameworks, and security/compliance requirements (critical in a regulated brokerage environment).
• Maintain a live backlog and roadmap for the business line's data requirements, visible to both business and data leadership.
• Represent the business line in data platform forums, ensuring the line's priorities are reflected in broader platform planning.

Ad-hoc and Business-as-Usual Support:

• Remain available to respond to ad hoc business requests, urgent data pulls, one-off analyses, regulatory or client queries, and incident troubleshooting for the assigned business line(s).
• Triage ad hoc asks against the planned roadmap, deciding what can be absorbed by the extended team versus handled directly.
• Act as an escalation point when data issues (quality, latency, discrepancies) impact business operations or reporting for the line.

Hands-on Technical Execution:

• Design and, where needed, personally build data pipelines and models on the Azure data stack (Data Lake, Data Factory, Event Hubs, Databricks).
• Build and review Power BI datasets, semantic models, and reports/dashboards, ensuring performance, row-level security, and a single source of truth for business metrics.
• Set data modelling standards (dimensional modelling, medallion architecture, naming conventions) for the business line and ensure the extended team adheres to them.

The core requirements for the job include the following:

Technical:

• Strong hands-on experience with the Azure data ecosystem: Azure Data Lake Storage, Azure Data Factory, Azure Event Hubs, and Azure Databricks (Spark/PySpark, Delta Lake, notebooks, and job orchestration).
• Proven experience in data engineering design and dimensional modelling, medallion/lakehouse architecture, batch and streaming pipeline design, data quality, and lineage practices.
• Strong hands-on experience with Power BI data modelling (star schemas), DAX, report/dashboard design, workspace and security (RLS) management, and performance tuning.
• Working knowledge of SQL and at least one programming language commonly used in data engineering (Python/PySpark preferred).
• Familiarity with CI/CD for data pipelines, version control (Git), and basic DevOps practices for data platforms is a strong plus.

Analytics and Business Use-Case Design

• Good grounding in building analytics use cases, from defining business metrics and KPIs to designing self-serve reporting and analytical marts.
• Ability to translate ambiguous business asks into well-scoped, testable analytics deliverables.
• Exposure to advanced analytics concepts (forecasting, segmentation, and risk/exposure analytics) is an advantage, particularly for CFD/trading-related lines.

Domain / Brokerage Industry Knowledge (preferred):

• Understanding of brokerage business lines such as CFDs, cash equities, and wealth/investment management trade lifecycle, order/execution data, positions and P& L, client onboarding and KYC, and portfolio and holdings data.
• Awareness of relevant regulatory and reporting considerations in brokerage/capital markets (e. g., trade/transaction reporting, client reporting, audit trails) and their implications for data design.
• Prior experience working in or with fintech, brokerage, asset management, or capital markets data teams is highly valued.

Stakeholder and Delivery Skills:

• Strong stakeholder management and communication skills, comfortable presenting to both business heads and technical leadership.
• Experience formalizing requirements (BRDs, user stories, data contracts) and running delivery in an agile or hybrid delivery model.
• Demonstrated ability to operate autonomously as an embedded, forward-deployed resource, balancing business proximity with technical rigor.
• Typically 3-7 years of experience spanning data engineering and/or analytics roles, with at least a few years in a lead, tech lead, or forward-deployed/embedded capacity.