About the Role

Index Exchange is looking for an experienced Engineering Manager / Engineering Lead to lead a team building the data products that turn large-scale marketplace data into capabilities consumed by customers, internal applications, operational teams, and machine learning systems.

Our Data Products organization sits between foundational data infrastructure and the consumers of data. We build the curated datasets, real-time data products, domain APIs, reporting experiences, integrations, and delivery mechanisms that make data usable as a product.

This is a highly technical leadership role. You will lead and develop a team of engineers while helping define the architecture and technical direction of systems spanning large-scale data processing, distributed systems, APIs, streaming, analytics, and product-facing serving.

The ideal candidate combines strong engineering leadership with deep technical experience building data-intensive products and distributed backend systems at scale.

What You'll Do

Lead a High-Impact Engineering Team

• Lead, mentor, and grow engineers working across backend systems, data processing, APIs, streaming, and analytical products.
• Establish strong engineering practices around architecture, testing, reliability, observability, operational ownership, and system design.
• Create an environment where senior engineers can independently lead complex technical initiatives.
• Partner with Product and Engineering leadership to translate business needs into a scalable technical roadmap.
• Balance near-term product delivery with investments in architecture, automation, reliability, and engineering velocity.

Build Data as a Product

Lead the development of reusable data products that turn large-scale marketplace data into trusted business capabilities.

You and your team will:

• Build curated and aggregated datasets supporting reporting, billing, operational workflows, experimentation, and ML use cases.
• Establish reusable aggregation frameworks and standardized patterns for creating new data products.
• Own correctness, reconciliation, discrepancy management, freshness, and SLAs for product-facing datasets.
• Design systems that allow new datasets, metrics, dimensions, and capabilities to be introduced quickly and safely.
• Drive self-service patterns that reduce one-off engineering work and make data capabilities reusable across the organization.

Build Real-Time Data Products

Help evolve Data Products beyond traditional batch analytics toward increasingly real-time product experiences.

• Build streaming-derived datasets and real-time operational data products.
• Design business-oriented transformations and aggregations over high-volume event streams.
• Define and operate freshness and availability guarantees for near-real-time products.
• Develop architectures that combine historical and real-time data where appropriate.
• Work with shared streaming and data platform teams while owning the business-facing products built on top of those capabilities.

Build APIs & Serving Experiences

Data should be accessible as a product rather than only through databases or BI tools.

You will help lead the development of:

• Domain-oriented data APIs.
• High-performance reporting and retrieval APIs.
• Serving layers combining historical and real-time datasets.
• Semantic and product-oriented data access patterns.
• API-first interfaces that allow applications and services to consume data without understanding the underlying data infrastructure.

You will drive clear contracts between producers and consumers and ensure these systems remain reliable as data volumes and use cases grow.

Modernize Reporting & Analytical Experiences

Lead the evolution of customer-facing and operational analytics from tightly coupled BI architectures toward more flexible API-first and serving-first experiences.

This includes:

• Customer-facing reporting products.
• Embedded analytics.
• Operational dashboards and troubleshooting experiences.
• Analytical interfaces used by internal and external consumers.
• Architectures that separate data products and semantic definitions from individual visualization technologies.

Build Integrations & Data Distribution Capabilities

Enable data to move reliably beyond the systems where it is generated.

• Build standardized integration patterns using APIs, event streams, connectors, webhooks, and external delivery pipelines.
• Develop scalable customer and partner data delivery capabilities.
• Support patterns such as data sharing, Reverse ETL, and API-based distribution.
• Establish consistent contracts, observability, governance, and operational guarantees across integrations.
• Drive self-service consumption patterns for downstream applications, partners, operational systems, and ML consumers.

Enable AI & ML Data Products

Partner with ML and product teams to make large-scale data easily consumable for intelligent systems.

• Build datasets and derived aggregates supporting ML workflows and experimentation.
• Develop approximate analytical products using techniques such as sketches, probabilistic aggregation, and frequency estimation.
• Enable AI-driven access to data through APIs, semantic retrieval, workflows, and emerging product experiences.
• Help establish scalable interfaces between data products and ML/AI systems.

Own Production Outcomes

Your team owns its products end-to-end.

You will drive:

• Reliability and SLA adherence.
• Observability and operational resiliency.
• Automated recovery and operational workflows.
• Dataset and API correctness.
• Reconciliation and discrepancy management.
• Capacity and performance planning.
• Release coordination and production readiness.
• Incident response and continuous improvement.

Success is measured not simply by pipelines delivered, but by the reliability, adoption, usability, and business impact of the products built on top of them.

What We're Looking For

Technical Experience

• Strong experience building and operating large-scale distributed or data-intensive systems.
• Strong understanding of data processing, distributed systems, APIs, and production system design.
• Experience with technologies such as Kafka, Spark, Flink, Kubernetes, Airflow, or comparable systems.
• Experience building backend services and APIs using languages such as Go, Java, Python, or similar.
• Experience with both batch and streaming architectures and the trade-offs between them.
• Strong understanding of data modeling, aggregation, partitioning, schema evolution, data correctness, and distributed processing.
• Experience designing reliable systems with clear SLAs, observability, and operational ownership.
• Experience building reusable platforms, frameworks, APIs, or abstractions rather than primarily one-off pipelines.

Experience with large-scale analytics, advertising technology, experimentation systems, ML infrastructure, customer-facing data products, or data integration platforms is a strong plus.

Leadership Experience

• Experience leading engineers responsible for complex production systems.
• Strong technical judgment and ability to lead architecture and system-design discussions.
• Ability to mentor senior engineers and raise the technical bar across a team.
• Demonstrated ability to balance delivery, technical debt, reliability, and longer-term architectural investments.
• Strong cross-functional leadership and ability to influence teams outside your direct organization.
• Ability to translate ambiguous business problems into clear technical direction.
• Strong written and verbal communication skills.

What This Team Owns

Data Products owns the layer that transforms foundational data capabilities into reusable business and customer experiences:

• Curated datasets and aggregations
• Real-time data products
• Domain and reporting APIs
• Analytical and reporting experiences
• Operational troubleshooting products
• Customer and partner integrations
• External data delivery
• Business-oriented transformations and semantic layers
• Product-level correctness, reconciliation, SLAs, and adoption

The team partners closely with Data Platform and Exchange/Data Systems teams that provide foundational capabilities including storage, query infrastructure, catalogs, ingestion frameworks, Kafka, Flink, workflow orchestration, and shared observability. Our job is not to rebuild those platforms. Our job is to turn them into products.

What Success Looks Like

A successful leader will help evolve Data Products from delivering individual datasets and reporting capabilities into an engineering organization that provides reusable, self-service, real-time, and API-driven data products.

You will help us:

• Dramatically reduce the time required to launch new data products.
• Increase reuse through common frameworks and standardized patterns.
• Improve freshness, correctness, reliability, and observability.
• Make real-time and historical data accessible through consistent interfaces.
• Reduce coupling between product experiences and individual storage or BI technologies.
• Enable customers, applications, operational systems, and ML workloads to consume data through well-defined products.
• Create engineering capacity by automating repetitive workflows.
• Unlock new value from Index's data across reporting, optimization, experimentation, integrations, and AI/ML.

Why you'll love working here:

• Comprehensive health, dental, and vision plans for you and your dependents
• Paid time off, health days, and personal obligation days plus flexible work schedules
• Competitive retirement matching plans
• Equity packages
• Generous parental leave available to birthing, non-birthing, and adoptive parents
• Annual well-being allowance plus fitness discounts and group wellness activities
• Commuter benefits and discounts, where available
• Employee assistance program
• Mental health first aid program that provides an in-the-moment point of contact and reassurance
• One day of volunteer time off per year and a donation-matching program
• Monthly town halls and regular community-led team events
• Multiple resources and programming to support continuous learning
• A workplace that supports a diverse, equitable, and inclusive environment - learn more here