Engineering Manager - Data

Turvo is a leading provider of a collaborative Transportation Management System (TMS) designed specifically for the supply chain industry. Their cloud-based platform connects freight brokers, 3PLs, shippers, and carriers, creating a unified supply chain ecosystem that enhances customer experiences through real-time collaboration and streamlined operations. By integrating internal and external systems, Turvo offers an end-to-end solution that improves analytics, automates business processes, and eliminates redundant manual tasks. The company serves a diverse customer base, including Fortune 500 logistics providers and small to mid-sized freight brokers. Turvo is headquartered in Dallas, Texas, with additional offices in Hyderabad, India.

Key Responsibilities:
- Lead and expand a high-performing Data, Analytics, and AI/ML engineering team by managing hiring, mentoring, career development, and performance evaluation.
- Own the delivery of data platforms and intelligent products, including streaming pipelines, ETL/ELT processes, data warehouse and lakehouse architectures, analytics APIs, and AI/ML-powered features such as automation and retrieval-augmented workflows.
- Collaborate closely with Product Management, Data Science, Analytics, and business stakeholders to translate supply chain and customer requirements into scalable roadmaps, milestones, and measurable outcomes.
- Define and enforce technical standards for system reliability, data quality, observability, security, and cost efficiency across both batch and real-time data systems.
- Oversee the design and implementation of event-driven and streaming architectures for data ingestion, processing, and delivery, as well as warehouse-centric modeling to support reporting, operational analytics, and ML feature consumption.
- Guide AI/ML engineering initiatives, including model serving integration, vector search, embedding indexing, metadata management, evaluation, and safe rollout of automated or AI-assisted product capabilities.
- Establish and promote engineering best practices such as code and design reviews, unit/integration/data pipeline testing, CI/CD pipelines for data and ML workloads, documentation, and runbooks.
- Work collaboratively with platform, application, and infrastructure teams on PostgreSQL and other data stores, defining service boundaries, SLAs, and cross-team integrations.
- Remove blockers, balance competing priorities, and improve team velocity while maintaining high-quality, predictable releases throughout the software development lifecycle, coordinating with QA and delivery partners.
- Serve as a technical thought leader and mentor, raising the bar on architecture, implementation, and operational excellence in data engineering, analytics engineering, and ML systems.

Required Qualifications:
- Over 10 years of hands-on experience building and operating data-intensive, distributed systems, with a proven track record of scaling both teams and platforms.
- At least 3 years of leadership experience managing 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 skills in Java and/or Python, with experience in microservices, Spring framework (where applicable), and service-oriented design.
- Deep expertise in data streaming technologies such as Kafka, ETL/ELT processes, orchestration, and cloud data warehouses like Snowflake, BigQuery, or Redshift.
- Hands-on experience with PostgreSQL and MongoDB, including data modeling, query optimization, performance tuning, and production operations for analytics, operational, or document-oriented workloads.
- Experience delivering AI/ML or LLM-enabled products in production, including pipelines, feature engineering, vector databases/search, embedding indexing, and API integration.
- Understanding of MLOps/LLMOps fundamentals such as versioning, monitoring, evaluation, guardrails, and incident response for data and model-backed services.
- Familiarity with unit testing, data quality assurance, and software engineering best practices in polyglot data and ML codebases.
- Excellent communication and cross-functional leadership skills, with the ability to simplify complex data and AI concepts for product and business stakeholders.

Preferred Qualifications:
- Experience with Hibernate or ORM-heavy Java services integrated with data platforms.
- Background in supply chain, logistics, or B2B SaaS domains.
- Exposure to modern data stack tools such as dbt, Spark, Flink, Airflow, or Dagster.

Turvo utilizes AI tools to assist in parts of the hiring process, including application review and resume analysis. These tools support but do not replace human judgment, and all final hiring decisions are made by humans.