Manager, Data Engineering

Position Summary:

We are seeking a Manager, Data Engineering to lead hands-on technical delivery for key components of our RealTime Customer Data Platform (CDP). This role is deeply focused on core data engineering: building scalable streaming pipelines, managing stateful processing, ensuring data quality and reliability, and operating production-grade data systems.

Primary Responsibilities:

RealTime CDP & Streaming:

• Architect, build, and operate high-throughput, low-latency streaming pipelines for clickstream, transactional, and behavioral data using Kafka, Flink, Spark Structured Streaming, or Dataflow/Beam.
• Design stateful stream processing for sectorization, windowing, deduplication, enrichment, and late-arriving data handling.

Core Data Engineering:

• Design and implement robust data models for events, customer attributes, and derived features.
• Manage schema evolution, backward compatibility, and data contracts. Build scalable batch pipelines to support backfills, reprocessing, and historical precomputation aligned with real-time logic.
• Data Quality, Reliability & Operations
• RealTime CDP & Streaming
• Audience Building & Segmentation
• Customer Data Products & Quality
• Leadership & Delivery
• Cross Functional Collaboration

Who We’re Looking For:

A hands-on, full stack Data Engineering leader who can elevate real time customer data and audience/segmentation capabilities, scale them globally, and develop worldclass engineering teams that deliver privacy safe, high-performance activation.

• Implement data quality checks (completeness, validity, timeliness), pipeline-level SLAs/SLOs, and end-to-end observability.
• Own production readiness including monitoring, alerting, capacity planning, and on-call support with SRE partners.
• Architect and scale real-time pipelines for clickstream, transactional, and behavioral data using Kafka, Flink, Spark Structured Streaming, or Dataflow/Beam.
• Design and evolve customer event models, session-inaction, and cross channel stitching to maintain a unified, channel stitching to maintain a unified, privacy aware customer view.
• Implement low latency activation APIs used by apps, web, CRM, loyalty, kiosks, and marketing orchestration platforms.
• Implement Eventing frameworks like managed Kafka or Confluent
• Establish standards for observability, SLAs/SLOs, schema evolution, lineage, and cost efficiency across streaming and batch paths.
• Engineer data pipelines and services that power audience segmentation, attribute computation, and activation feeds.
• Ensure efficient data delivery to downstream systems (e.g., CDPs, marketing platforms, APIs) with strict latency and freshness guarantees.
• Build dynamic audience services for behavioral and lifecycle cohorts, rules driven propensity groupings, and event triggered real-time segments.
• Define data contracts and versioning for attributes, traits, and segment definitions to ensure reuse, durability, and safe change management.
• Set and abide by audience governance standards (freshness SLAs, recency/frequency windows, cardinality limits, consent gates) and ensure they’re consistently enforced.

Qualifications

• 7-10 years in largescale Data Engineering / Distributed Systems.
• 5+ years with GCP or AWS (GCP preferred).
• 3+ years working on real time customer data and/or segmentation platforms.
• Experience with CDPs (mParticle, Adobe RTCDP, Braze, Tealium).
• Designing real-time audience builders, rule engines, and activation frameworks.
• Multiregional deployments, data residency, and consent management at global scale.
• Strong stakeholder communication; ability to simplify technical concepts for marketers and product leaders.
• Systems thinker with strong architectural judgment and influence.