Lead and grow a high-performing Data, Analytics, and AI/ML engineering team through hiring, mentoring, career development, and performance management.
Own delivery for data platforms and intelligent products: streaming pipelines, ETL/ELT, data warehouse and lakehouse patterns, analytics APIs, and AI/ML-powered features (including automation, assistants, and retrieval-augmented workflows).
Partner with Product Management, Data Science, Analytics, and business stakeholders to translate supply-chain and customer needs into scalable roadmaps, milestones, and measurable outcomes.
Drive technical direction and standards for reliability, data quality, observability, security, and cost across batch and real-time systems.
Oversee design and implementation of event-driven and streaming architectures (ingestion, processing, delivery) and warehouse-centric modeling for reporting, operational analytics, and ML feature consumption.
Guide delivery of AI/ML engineering initiatives: model serving integration, vector search, embedding indexing, metadata and chunking strategies, evaluation, and safe rollout of automated/AI-assisted product capabilities.
Establish engineering practices: code and design reviews, unit/integration/data pipeline testing, CI/CD for data and ML workloads, documentation, and runbooks.
Collaborate with platform, application, and infrastructure teams on PostgreSQL and other data stores, service boundaries, SLAs, and cross-team integrations.
Remove blockers, balance priorities, and improve team velocity while maintaining high quality and predictable releases across the SDLC (including coordination with QA and project/delivery partners).
Act as a technical thought leader and default mentor for engineers on data engineering, analytics engineering, and ML systems—raising the bar on architecture, implementation, and operational excellence.
10+ years of hands-on experience building and operating data-intensive, distributed systems, with a track record of scaling teams and platforms (horizontally and vertically).
3+ years leading engineers (or equivalent scope) in data engineering, analytics engineering, ML engineering, or platform/data teams.
Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
Strong programming background in Java and/or Python; experience with microservices, Spring (where applicable), and service-oriented design.
Deep experience with data streaming (e.g., Kafka or similar), ETL/ELT, orchestration, and cloud data warehouses (e.g., Snowflake, BigQuery, Redshift, or comparable).
Hands-on experience with PostgreSQL and MongoDB—modeling, query patterns, performance tuning, and operating them in production (including analytics, operational, or document-oriented workloads).
Experience shipping AI/ML or LLM-enabled products in production: pipelines, feature patterns, vector databases/search, embedding indexing, and integration with application APIs.
Understanding of MLOps / LLMOps basics: versioning, monitoring, evaluation, guardrails, and incident response for data and model-backed services.
Comfort with unit testing, data quality checks, and software engineering best practices in polyglot data/ML codebases.
Excellent communication and cross-functional leadership; able to simplify complex data and AI concepts for product and business partners.
Hibernate or ORM-heavy Java services integrated with data platforms.
Supply chain, logistics, or B2B SaaS domain experience.
Exposure to dbt, Spark, Flink, Airflow/Dagster, or similar modern data stack tools.
