Lead Engineer, Market Data

MIO Partners Inc. (MIO) is a global, world-class investment and advisory institution with a decades-long track record of performance for our clients through multiple market cycles. Our team of approximately 250 people provides asset management and advisory services to current and former McKinsey employees. We manage more than $20 billion in assets across public and private markets. We are headquartered in New York City, with offices in Atlanta, Georgia; West Palm Beach, Florida; Germany; Hong Kong; Singapore; Spain; and the UK.

We provide objective advice on long-term wealth building and create distinctive investment products that deliver value relative to market-based benchmarks. We operate two core lines of business: (i) our advisory team provides advice, wealth planning, and retirement services to approximately 2500 individual clients, and (ii) our investments business consists of alpha-seeking alternative investment strategies across public and private markets.

A defining characteristic of our business is our perfect alignment with our clients. Our extraordinary mandate as a boutique investment and advisory firm, grounded in a core set of principles, enables us to create unique products and services while keeping our clients' interests front and center.

Much of our success can be attributed to our insatiable intellectual curiosity. We seek to hire individuals who are passionate about our mission and values, and who always strive toward excellence. We care about fostering a culture of continual improvement, as individuals and as an organization, and are excited to work with individuals who have a similar growth mindset. We are proud to have a culture that promotes the highest ethical standards and investor-focused values, alongside a commitment to diversity and inclusivity and a highly collaborative work environment.

MIO (and related entities) is an independent, indirect, and wholly owned subsidiary of McKinsey & Company.

Position

MIO is seeking a Lead Engineer to drive the evolution of our market data platform. This role sits within the Research & Investment Technology team and reports directly to the Director of Research & Investment Technology.

This is a senior, hands-on leadership role responsible for assessing the current architecture and leading the design and build-out of a next-generation, scalable data platform.

You will own the end-to-end ingestion, modeling, and distribution of market data across multiple vendors (e.g., Bloomberg, LSEG, ICE), while guiding architectural decisions, mentoring engineers, and establishing best practices across data engineering and platform design.

Primary responsibilities

Architecture Review & Platform Build-Out

• Assess the current market data architecture, identify gaps, and define a target-state architecture in collaboration with the Director of Research & Investment Technology
• Lead the design and implementation of a scalable, resilient, and extensible data platform
• Establish architectural principles, standards, and best practices across data engineering
• Drive re-architecture initiatives to improve performance, scalability, and maintainability

• Lead evaluation and selection of core data platform tools and frameworks, including ETL/ELT pipelines, workflow orchestration (e.g., Airflow), data storage (lakes, warehouses), and caching layers (e.g., Redis)
• Make build vs. buy decisions and define the long-term data technology stack
• Standardize tooling and promote engineering best practices across the team

• Architect and build robust ETL/ELT pipelines for ingesting and processing data from multiple vendors (e.g., Bloomberg, LSEG, ICE, etc.)
• Design workflows for data normalization, symbology mapping, enrichment, and distribution
• Implement monitoring, alerting, and failure recovery mechanisms
• Work hands-on with large-scale financial datasets

• Lead the design of scalable data models for market and reference data
• Define canonical data models and schema standards across asset classes
• Optimize database design for performance, flexibility, and analytical use cases
• Design systems to handle vendor-specific schemas, symbology mapping, and entitlements

• Design and implement caching strategies (e.g., Redis) to support low-latency data access
• Optimize data access patterns and overall system performance
• Balance real-time and batch processing requirements

• Partner with DevOps to design and manage cloud-native data platforms using AWS services (e.g., S3, Lambda, Glue, EMR, Redshift, ECS/EKS)
• Optimize infrastructure for cost, performance, and scalability

• Lead and mentor a team of data engineers, setting technical direction and standards
• Guide the team on tool selection, architectural decisions, and implementation approaches
• Conduct code and design reviews with a focus on scalability and maintainability
• Foster a culture of ownership and engineering excellence

• 10-12+ years of experience in data engineering, data architecture, or related fields
• Proven experience evaluating and re-architecting data platforms at scale
• Strong hands-on experience building distributed data pipelines and systems
• Deep expertise in AWS-based data architecture
• Strong programming and data engineering skills, including Python, SQL, workflow orchestration tools (e.g., Airflow), and ETL/ELT frameworks
• Extensive experience in database design and data modeling, including time-series and financial data, normalized and denormalized schemas, and relational and columnar databases
• Experience with caching technologies (e.g., Redis) and performance optimization
• Solid understanding of financial markets and asset classes, including fixed income, commodities, equities and derivatives, and digital assets
• Experience leading engineering teams and driving architectural decisions
• Strong familiarity with market data vendors such as Bloomberg, LSEG, and ICE

• Experience in a multi-strategy hedge fund, asset manager, or leading market data provider (e.g., Bloomberg, LSEG, ICE Data Services)
• Familiarity with real-time and streaming architectures (e.g., Kafka, Kinesis)
• Experience with data governance, lineage, and data quality frameworks
• Experience building canonical data platforms or enterprise data layers

• Strong architectural vision with hands-on execution capability
• Deep expertise in data modeling and distributed system design
• Ability to evaluate and select appropriate tools and technologies
• Strong leadership, mentoring, and team-building skills
• High degree of ownership and accountability