Director, Data Strategy & Engineering

Responsibilities

• Define and own the firm’s data platform strategy spanning cloud data architecture, AI data infrastructure, master data management and the analytics capability roadmap
• Drive architectural decisions for the Portage Point data ecosystem including data lakehouse design (bronze / silver / gold medallion architecture), real-time versus batch processing trade-offs, semantic layer strategy, data mesh adoption and AI-ready data product frameworks
• Own the data architecture review function, ensuring all new AI products and data pipelines meet architectural, security and governance standards before development commences
• Execute the data strategy that enables the AI Solutions product roadmap, defining how data is structured, labeled, versioned and served to AI / ML models across consulting use cases including CDD / FDD automation, value creation tracking, working capital optimization, restructuring scenario engines, portfolio benchmarking and MIS intelligence layers
• Drive the development of proprietary financial and industry data assets including training data curation, golden datasets for model evaluation and benchmarking databases
• Design knowledge graph strategy, capturing structured relationships between companies, deal comparables, management teams and operational KPIs
• Oversee design and operation of scalable ELT / ETL pipelines ingesting structured and unstructured data from diverse sources including financial data providers (Capital IQ, Refinitiv, PitchBook, Bloomberg), client ERP / CRM systems, SEC filings, management packs and market research
• Own engineering layer powering consulting analytics including financial benchmarking, working capital KPI dashboards, EBITDA bridge models and cost structure analyses
• Lead and develop a team of Vice Presidents and Senior Associates (approximately 10 to 15 professionals) across data engineering, analytics engineering and data governance
• Partner with Human Capital Management to drive Data & Engineering talent acquisition success
• Drive the data governance framework including policies, standards, stewardship model and compliance controls for handling client deal data, portfolio company data and third-party data, in alignment with NDA and regulatory obligations
• Oversee data security architecture in collaboration with the security function, with particular attention to AI training data handling, model output retention and deal data segregation
• Build and operate data quality programs including automated monitoring and anomaly detection while proactively flagging data issues
• Partner with global leadership to translate firm priorities into a sequenced data platform roadmap with measurable business outcomes
• Identify and shape data monetization opportunities including proprietary benchmarking databases, index products and data-as-a-service offerings, and partner with firm leadership to commercialize proprietary data assets as consulting IP and competitive differentiators
• Serve as a thought leader on data strategy in deal advisory and portfolio management, engaging in senior client conversations on data room analytics, portfolio data standardization and data-driven value creation and representing the firm at internal forums and external industry events where appropriate
• Manage relationships with third-party data providers, evaluate new data sources for relevance to consulting use cases and lead the evaluation and adoption of emerging data technologies including data mesh, federated query engines and AI-native data platforms
• Lead firm-wide knowledge sharing, training and change management efforts to scale data and analytics capability across practice lines
• Support business development by shaping data-related proposals, client presentations and solution demonstrations
• Support talent acquisition and firm-building initiatives
• Contribute to a high-performing, inclusive and values-driven culture

Qualifications

• Bachelor’s of Technology / Engineering and / or Master's in Computer Science, Data Engineering, Information Systems, Statistics or related technical field from a top undergraduate program; MBA with quantitative focus strongly preferred
• Willing to relocate to or be primarily present in Gurgaon
• Ability to collaborate across global time zones, including US-based teams
• Ten to fifteen years of progressive experience in AI / ML, software engineering or related technical disciplines, with a meaningful portion of that experience in management consulting, professional services or private equity-backed advisory environments
• Demonstrable track record of defining and executing enterprise data platform strategy, not just managing engineering teams, including direct experience setting architectural direction and making build-versus-buy decisions for data platforms
• Track record building and scaling teams, including direct hiring leadership and experience managing vendor or offshore delivery partners
• Expert-level mastery of cloud data platforms (Snowflake, Databricks, BigQuery) at production scale, including familiarity with Delta Lake and Apache Iceberg
• Deep experience with real-time and batch data architectures including Apache Kafka, Apache Flink, Airflow and Prefect
• Strong proficiency with analytics engineering tooling (dbt), semantic layer tools, advanced SQL, Python and PySpark
• Hands-on experience designing and implementing enterprise data governance programs including data cataloguing, data lineage, data contracts, metadata management and data quality frameworks such as Great Expectations
• Expert understanding of the intersection of data engineering and AI / ML including feature stores, embedding pipelines, vector databases (Pinecone, Weaviate, pgvector), graph databases (Neo4j) and model data versioning
• Strong working knowledge of financial data structures (P&L, balance sheet, cash flow), accounting standards, private equity reporting conventions and financial data providers (Capital IQ, Refinitiv, Bloomberg, PitchBook)
• Demonstrated ability to establish and enforce data governance, security and compliance standards for confidential deal and portfolio data
• Ability to evaluate, challenge and approve architecture decisions made by Vice President and Senior Associate level engineers
• Experience operating in client-facing or executive-facing settings with the ability to shape data-related proposals, presentations and senior stakeholder conversations
• Superior written and verbal communication skills, including executive-ready presentation and reporting skills
• Proven ability to thrive in lean, fast-moving teams
• High attention to detail, responsiveness and ownership mindset…