Distinguished Software Engineer

Walmart

Distinguished Software Engineer providing technical leadership and architectural design for Walmart's ML data and feature store platform. Delivering scalable and reliable data systems for diverse applications.

Posted 8/1/2026full-timeBangalore • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and optimizing ML data and feature store platforms, with a strong focus on data quality, observability, and reliability. Proficient in Java and Python SDK design, distributed systems, and high-performance data processing pipelines.

Highest-signal resume keywords

Java SDK/Library DesignPython SDK Design for Data ScienceDistributed Systems FundamentalsObservability Tooling ExperienceKubernetes Deployment at Scale

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Hard Skills

JavaPythonData ModellingBatch Data PipelinesLow-Latency Data StoresGRPC Service DesignCI/CD Pipeline DesignObservability FrameworksArchitectural StandardsReliability Engineering

Soft Skills

MentorshipCross-Functional CollaborationTechnical Authority

Tools & Technologies

SparkHiveAirflowOpenTelemetryPrometheusGrafanaKubernetesGCPAzureRedis

Industry Keywords

Machine LearningData EngineeringFeature StoreDistributed SystemsObservabilityData Quality StandardsMulti-Cloud StrategiesProduction Software DevelopmentLatency OptimizationSchema Design

Tech Stack

Tools & technologies

AirflowAzureCassandraCloudDistributed SystemsGoogle Cloud PlatformGrafanaGRPCJavaKubernetesPrometheusPythonRedisSpark

About the role

Key responsibilities & impact

Define and own the short term and long-term technical vision for the ML data and feature store platform, spanning offline pipelines, online serving, and the infrastructure that connects them
Identify and systematically retire architectural debt across the platform — prioritising by production impact, latency, reliability, and engineering velocity
Serve as the primary technical authority in cross-functional design reviews involving ML engineering, data engineering, platform, and product teams
Influence the broader engineering organisation through published architectural standards, internal RFCs, and mentorship of senior and staff engineers
Architect end-to-end feature store data systems — from data ingestion and feature computation through materialization into online stores and delivery to model inference
Define and Design high performant, scalable, reliable Data and Control Mgmt planes for both online and offline data processing pipelines
Define and govern data quality standards, SLOs, and observability frameworks across the platform
Drive platform decisions around schema design, storage backend selection, consistency trade-offs, and multi-cloud data replication strategies
Design and optimise large-scale batch data pipelines using Spark, Hive, or equivalent
Establish pipeline reliability and observability standards: data freshness SLOs, quality checks at ingestion, deduplication, late-arrival handling, and lineage tracking
Provide guidance on orchestration and scheduling of batch workloads (Airflow or equivalent)
Architect and optimise online feature serving infrastructure targeting high QPM at consistently low latency
Define online serving reliability patterns and establish capacity planning and load testing practices for online stores
Lead the design and evolution of the platform's Java and Python SDKs — from the current thick SDK to a thin SDK

Requirements

What you’ll need

Bachelor's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 6 years’ experience in software engineering or related area
8 years’ experience in software engineering or related area
10+ years of production software development experience, with demonstrable ownership of complex, distributed systems
Java (Expert): Concurrency primitives (AtomicReference, CAS, CompletableFuture, ForkJoin), SDK/library design, API versioning, backward compatibility guarantees, and dependency management
Python (Strong): Production-grade library and SDK design for data science or ML engineering consumers; idiomatic async client patterns; packaging and dependency management
Strong data modelling instincts across relational, wide-column, key-value, and columnar storage paradigms
Experience with observability tooling: distributed tracing (OpenTelemetry), structured logging, metrics (Prometheus/Grafana or equivalent), and SLO-based alerting
Experience deploying and operating production services on Kubernetes at scale (GKE, AKS, or equivalent)
Familiarity with GCP and/or Azure infrastructure: managed storage services, multi-cloud networking, IAM/RBAC, and cloud-native observability tooling
Understanding of CI/CD pipeline design for data and ML workloads — including pipeline testing strategies, staged rollouts, and data migration patterns
Proven experience designing and operating services at high QPM with p99 latency targets in the low-millisecond range, across distributed, multi-region infrastructure
Deep expertise in low-latency data stores: Cassandra and Redis
Expert-level understanding of distributed systems fundamentals: CAP theorem, consistency models, replication topologies, partitioning strategies, and eventual consistency trade-offs
Deep expertise in reliability engineering for online systems: circuit breakers, exponential backoff with jitter, bulkhead patterns, rate limiting, local vs. remote DC failover, and graceful degradation under store-side pressure
Experience with gRPC service design at scale: service contract definition, proto schema evolution, deadline propagation, streaming patterns, and observability

Benefits

Comp & perks

incentive awards for your performance
maternity and parental leave
PTO
health benefits
flexible work arrangements