Lead Data Engineer - AI/ML Focus

Overview

Lead data engineer building AI/ML platforms and scalable data pipelines

Key Responsibilities

• cloud optimization
• data engineering
• mlops pipelines
• feature store
• data governance
• real-time analytics

Tasks

-Optimize cloud cost, performance, and reliability for large-scale AI/ML workloads. -Drive innovation in AI/ML data engineering and real-time analytics. -Ensure compliance with SOC2, GDPR, PII standards based on company needs. -Guide data engineers on best practices, code quality, and scalable data engineering patterns. -Lead and mentor data engineering teams. -Implement model monitoring, data quality, feature versioning, and automated retraining. -Drive standards for cloud data infrastructure and reusable data engineering components. -Build ML-ready data environments, feature stores, and training pipelines. -Translate business requirements into scalable data strategies. -Partner with data scientists to productionize ML models with CI/CD/CT. -Define and execute enterprise data strategies aligned with AI/ML initiatives while championing best practices in data engineering, MLOps, and cloud optimization. -Build high-quality data models (dimensional, data vault, lakehouse). -Architect MLOps pipelines using Docker, Kubernetes, Terraform, MLflow, SageMaker, or Vertex AI. -Lead design of scalable data pipelines, ingestion frameworks, and distributed processing systems. -Collaborate with data scientists, ML engineers, and business stakeholders to deliver impactful solutions. -Implement observability, lineage, and data quality frameworks across all pipelines. -Maintain metadata, cataloging, governance processes (Collibra, Alation, Unity Catalog). -Develop scalable ELT/ETL pipelines using Spark, PySpark, SQL, Airflow, DBT, Kafka, Kinesis. -Ensure quality, compliance, and security across all data platforms while implementing observability, lineage, and governance frameworks. -Support real-time and batch feature engineering and inference pipelines. -Implement secure data-sharing, encryption, IAM, tokenization, and access patterns. -Champion emerging technologies including GenAI, vector databases, and LLM-based pipelines.

Requirements

• python
• spark
• databricks
• snowflake
• aws
• mlops

What You Bring

-Background in real-time analytics and low-latency ML inference. -Strong programming in Python, SQL; deep expertise in Spark/Databricks. -Architect enterprise data lake/lakehouse/warehouse solutions (Databricks, Snowflake, BigQuery, Redshift). -Bachelor’s degree in Computer Science or related field, or similar, or equivalent experience. -Experience implementing vector databases (Pinecone, FAISS, Milvus) and LLM-based pipelines including RAG. -Any Data or AI/ML related certifications. -6–12+ years of data engineering experience with 2–5+ years in a lead role. -Own end-to-end execution of data engineering initiatives, including estimation, delivery, and performance optimization. -Expertise with cloud platforms (AWS, Azure, or GCP). -Experience building ML-ready architectures, feature stores, and MLOps pipelines. -Proven ability to lead engineering teams, mentor junior engineers, and drive architectural decisions. -Experience in highly regulated industries (healthcare, fintech, retail, AEC, manufacturing).

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Benefits

-Flexible Spending Account (FSA) ​ -Health Savings Account (HSA) ​ -Life, AD&D, short-term, and long-term disability ​ -Generous paid time off​ -Professional and personal development ​ -401 (k) Retirement Plan ​ -Medical, dental, vision insurance ​ -Commuter and wellness benefits

About Michael Baker International

-Specializes in transportation, water resources, energy, and government-related projects, delivering high-quality solutions. -Known for its expertise in complex, multi-disciplinary projects that shape cities, regions, and transportation networks. -Works on a diverse range of projects including roadways, bridges, airports, water systems, and energy infrastructure. -Regularly supports local, state, and federal government agencies with crucial public infrastructure projects. -The firm’s legacy is built on delivering practical, sustainable, and forward-thinking solutions to challenging engineering problems. -Notable projects include major transportation systems, environmental remediation, and disaster recovery operations. -Employs cutting-edge technology and innovative approaches to solve the most pressing engineering challenges of today.

Sector Specialisms