Lead Data Engineer - AI/ML Focus

Michael Baker International•Denver, CO

27d•$130,000 - $170,000

About The Position

Michael Baker International is seeking a highly skilled Lead Date Engineer with deep AI/ML expertise to architect, build, and scale intelligent, data-driven applications across our enterprise ecosystem. As the Lead Data Engineer, you will architect, build, and optimize enterprise-grade data platforms that power AI/ML products, analytics, and automation initiatives. You will lead data engineers, partner with data scientists, and own the roadmap for scalable data systems that enable real-time insights and model-driven decision-making.

Requirements

Bachelor’s degree in Computer Science or related field, or similar, or equivalent experience.
Any Data or AI/ML related certifications.
6–12+ years of data engineering experience with 2–5+ years in a lead role.
Strong programming in Python, SQL; deep expertise in Spark/Databricks.
Experience building ML-ready architectures, feature stores, and MLOps pipelines.
Expertise with cloud platforms (AWS, Azure, or GCP).
Proven ability to lead engineering teams, mentor junior engineers, and drive architectural decisions.

Nice To Haves

Experience implementing vector databases (Pinecone, FAISS, Milvus) and LLM-based pipelines including RAG.
Background in real-time analytics and low-latency ML inference.
Experience in highly regulated industries (healthcare, fintech, retail, AEC, manufacturing).

Responsibilities

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