Engineering Manager - Data

About Turvo: Turvo provides a collaborative Transportation Management System (TMS) application designed specifically for the supply chain. Turvo Collaboration Cloud connects freight brokers, 3PLs, shippers, and carriers to unite supply chain ecosystems, delivering outstanding customer experiences, real-time collaboration, and accelerated growth. The technology unifies internal and external systems, providing one end-to-end solution that streamlines operations, enhances analytics, and automates business processes while eliminating redundant manual tasks.

Turvo’s customers include some of the world’s largest Fortune 500 logistics service providers and shippers as well as small to mid-sized freight brokers. Turvo is based in Dallas, Texas, with offices in Hyderabad, India. ( www.turvo.com ). Responsibilities: 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. Qualifications: 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. Nice to have: 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.