Manager, Data Engineering

About McDonald’s

Department Overview

Skills Required

• Kafka, Confluent, Pub/Sub, AWS, Azure, GCP, Docker, Kubernetes, CICD, CI/CD, Java Experience Range: 7 - 12 years

Duties

• 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. Work location: Hyderabad, India Work pattern: Full time role. Work mode: Hybrid 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. • Partner with Mar Tech and Product to templatize audience playbooks (e.g., reactivation, onboarding, churn risk, high value, cart abandon). • Implement comprehensive data quality, validation, and lineage across all audience and profile pipelines. • Create reference patterns and templates so global markets and channels can integrate quickly and safely. • Drive CDP capabilities and roadmaps, manage dependencies, and deliver against business outcomes with clear KPIs and executive reporting.

Requirements

• Kafka, Confluent, Pub/Sub, AWS, Azure, GCP, Docker, Kubernetes, CICD, CI/CD, Java Experience Range: 7 - 12 years