Senior AI & Data Engineer

Position Summary

We are seeking a Senior Data Engineer to design, build, and optimize scalable data infrastructure that powers business intelligence, analytics, and AI-driven applications. The ideal candidate brings great communication skills, deep expertise in Python, containerization, cloud-native architectures, and modern data warehousing, with a growing interest in AI agents and emerging integration patterns.

• Design and implement robust, scalable ETL/ELT pipelines to ingest, transform, and deliver data across the organization

• Build and maintain data models optimized for analytical workloads and downstream consumption

• Develop reusable frameworks and libraries to accelerate data engineering workflows

• Architect and manage cloud-native data solutions on AWS, leveraging services such as S3, EC2, ECS, ECR and Event Bridge

• Deploy and orchestrate containerized workloads using Docker and Kubernetes in production environments

• Implement infrastructure-as-code practices using Terraform Cloud Formation, or similar tools

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• Design and optimize data warehouse architectures using Redshift or Snowflake

• Develop efficient data models, partitioning strategies, and query optimization techniques

• Manage data lifecycle, governance, and cost optimization within warehouse environments

• Explore and integrate AI agents and Model Context Protocol (MCP) patterns into data workflows where applicable

• Collaborate with AI/ML teams to ensure data infrastructure supports model training, inference, and feature engineering needs

• Stay current with emerging data and AI technologies, evaluating their potential business impact

• Partner with analytics, product, and engineering teams to understand data requirements and deliver solutions

• Mentor junior engineers and contribute to team best practices, code reviews, and technical documentation

• Participate in architectural decisions and contribute to the data platform roadmap

• 7+ years of professional experience in data engineering or related roles

• Proven track record building and operating production data pipelines at scale

• Python: Advanced proficiency including data libraries (pandas, PySpark, SQL Alchemy), testing frameworks, and packaging

• Containerization & Orchestration: Strong hands‑on experience with Docker and Kubernetes (EKS, GKE, or self‑managed clusters)

• Data Warehousing: Deep expertise with Redshift or Snowflake, including performance tuning, data modeling, and administration including DBT

• AWS: Extensive experience across the AWS ecosystem, particularly data‑related services (S3, Glue, Lambda, IAM, VPC, Cloud Watch)

• SQL: Expert‑level SQL skills for complex analytical queries and data transformations

• Experience with workflow orchestration tools (Airflow, Dagster, etc.)

• Familiarity with version control (Git), CI/CD pipelines, and agile development practices

• Strong understanding of data governance, security, and compliance principles

• Experience with or exposure to AI agents, LLM integrations, or Model Context Protocol (MCP) implementations

• Background in real‑time streaming architectures (Kafka, Kinesis, Flink)

• Experience with dbt for transformation layer management

• Familiarity with data observability and quality tools (Monte Carlo, Great Expectations, dbt tests)

• Knowledge of additional programming languages (Scala, Java, Go)

• Experience in professional services, legal tech, or enterprise SaaS environments

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; or equivalent practical experience

• Relevant certifications a plus (AWS Data Analytics, Snowflake Snow Pro, Kubernetes CKA/CKAD)

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  Unlock your potential with professional development opportunities supported by a community of experienced professionals. We offer reimbursement for training and continuing education to help you stay ahead in your career.

• Enjoy Where You Work: Thrive in our modern, open offices designed to inspire creativity and collaboration.…