Principal, Head of Data Platforms

Position Overview

We are seeking an exceptional Principal / Head of Data Engineering toestablishand lead our data engineering function from the ground up. This role reports to theHead of Data and AI Engineeringandis responsible forthe complete design, development, and implementation of a world-class modern data platform. You will drive the strategic evolution of our data infrastructure, enabling both structured and unstructured data workflows atscale. You will spearhead the upgrade and modernization of our existing Azure Data Factory pipelines to next-generation orchestration tools, implement efficient data ingress and egress patterns, establish AI/LLM-native data capabilities through advanced vector indexing and streaming architectures, and build a strong data engineering organization from the ground up. You will collaborate closely with cloud engineering, network engineering, and data products teams to architect a unified data lake and comprehensive data governance framework that supports diverse analytical and operational needs across our portfolio.

Key Responsibilities

Organization Building & Team Leadership

• Build and scale the data engineering organization frominception, defining team structure, roles, and responsibilities across the function

• Establish engineering culture emphasizing technical excellence, collaboration, ownership, and continuous learning

• Recruit, mentor, and develop high-performing data engineers withexpertisein modern data platforms, ETL/ELT, orchestration, streaming, and vector databases

• Partner with Human Resources on recruitment strategy, hiring processes, and organizational scaling as the firm grows

Strategic Vision & Roadmap

• Establish a comprehensive, multi-year data engineering strategy aligned with firm objectives

• Define technical roadmaps for data infrastructure, platform capabilities, and technology adoption

• Establish governance frameworks for data engineering decisions, standards, and best practices

• Lead technology evaluation and vendor selection processes with clear ROI and strategic fit

Platform Architecture & Modernization

• Design and architect a modern, scalable data platform leveraging Databricks on Azure that supports both structured and unstructured data at petabyte scale

• Lead the modernization of legacy Azure Data Factory (ADF) pipelines to production-grade orchestration platforms such as Prefect, or Apache Airflow

• Develop a comprehensive upgrade and migration roadmap for ETL/ELT pipelines, ensuring zero data loss, minimal downtime, and improved observability

• Lead the implementation of serverless and Zero ETL patterns toeliminateinfrastructure management overhead and reduce time-to-insight

• Own cost optimization initiatives across the data platform, balancing performance, reliability, and operational efficiency

ETL/ELT & Orchestration Excellence

• Build deepexpertisein Directed Acyclic Graph (DAG) principles and modern workflow orchestration patterns for reliable, scalable pipeline management

• Evaluate, select, and implement best-in-class orchestration tools (Prefect, Airflow) that provide superior visibility, error handling, and data lineage tracking

• Establish patterns for dynamic DAG generation, conditional execution, and advanced error recovery strategies

• Design and enforce data quality frameworks within orchestration tools to catch issues at the pipeline level

• Create monitoring, alerting, and observability solutions for 100%+ visibility into pipeline health and data freshness

Data Movement & Integration Patterns

• Architect efficient data ingress patterns supporting high-volume, real-time, and batch data inflows from diverse sources (APIs, databases, cloud services, SaaS platforms)

• Design sophisticated data egress patterns enabling secure, efficient data distribution to downstream systems, analytics tools, and external stakeholders

• Implement change data capture (CDC) patterns and incremental processing strategies tooptimizeresource usage and reduce latency

• Establish governance frameworks for data movement including encryption, authentication, and audit trails

Streaming & Real-Time Data Capabilities

• Evaluate and implement streaming platforms (Kafka, Event Hubs, Kinesis) to support real-time analytics and operational use cases

• Design event-driven architectures that enable low-latency decision-making and automated workflows

• Build streaming ingestion pipelines that efficiently funnel data into thelakehousewhilemaintainingdata quality and lineage

AI & LLM-Native Data Infrastructure

• Design and build vector database infrastructure to support LLM applications, including efficient indexing, similarity search, and retrieval-augmented generation (RAG) workflows

• Establish patterns for embedding generation, vector storage optimization, and integration with vector databases

• Build data pipelines that prepare unstructured data (documents, images, audio) for embedding and LLM consumption

• Create governance and provenance tracking for embeddings and vector data to ensure transparency and compliance

Data Lake & Catalog Implementation

• Lead the development and governance of a unified data lake,establishingdata quality standards, lineage tracking, and compliance frameworks

• Support implementation ofa modern data catalog solution that enables data discovery, governance, and self-service analytics across the enterprise

• Establish data engineering best practices, testing frameworks, production deployment pipelines, and operational standards

Cross-Functional Collaboration & Stakeholder Management

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