Team Lead Data Engineering

Overview

We are a company specializing in AI-powered enterprise operations, delivering digital solutions and consulting services that drive value and transform businesses. The focus is on leveraging advanced technology to streamline operations, improve efficiency, and unlock new revenue opportunities, particularly within private capital markets.

The ecosystem includes an AI-native PaaS platform designed to optimize workflows and surface insights, a high-performance SaaS cloud solution for scalable execution, and modular technology playbooks supporting business growth and optimization. With over a decade of experience supporting high-growth and private equity-backed companies, the organization turns technology into a strategic advantage.

The Opportunity

This role is ideal for a senior-level Data Engineering leader with strong expertise in cloud data architecture, data modeling, SQL, and data governance for enterprise-scale systems.

You will lead the design and implementation of data architectures supporting operational, analytical, and AI/ML workloads, while also mentoring a team and driving best practices across the organization.

Key Responsibilities

Architecture & Strategy: Design and maintain enterprise data architecture strategies and blueprints. Build cloud-native solutions on AWS (Redshift, RDS, Glue, Lake Formation) or equivalent platforms.
Data Modeling: Define and enforce standards for dimensional modeling, denormalized schemas, OLTP/OLAP patterns, and AI-ready data structures.
Transformation & Orchestration: Design data transformation layers using DBT and oversee orchestration with tools like Airflow or Prefect across batch, streaming, and event-driven pipelines.
Quality & Governance: Establish data validation, quality control, and testing frameworks. Maintain governance practices for lineage, cataloging, and access control.
AI/ML Support: Ensure data platforms support downstream AI/ML use cases, including feature stores, embeddings, and training datasets.
Leadership & Mentorship: Lead, mentor, and develop team members. Drive technical decisions and conduct architecture and code reviews.

Requirements

Experience: 7+ years in data engineering or data architecture roles.
Cloud: 5+ years designing cloud-based architectures (AWS, GCP, or Azure).
SQL & ETL: Strong experience writing complex SQL and building ETL/ELT pipelines using tools such as Airflow or Prefect.
Modern Tooling: Hands-on experience with DBT for transformation and documentation.
Data Design: Expertise in data warehouse design (OLTP, OLAP, star/snowflake schemas) and modeling methodologies.
Governance: Strong understanding of data governance, metadata management, and quality frameworks.
Leadership: Proven experience managing and mentoring teams.
Education: Bachelor’s degree in Computer Science or a related field (preferred).

Preferred Skills (Nice to Have)

Experience with Python (Pandas, PySpark).
Familiarity with Docker and Kubernetes.
Experience with CI/CD pipelines and AWS services like Lambda or Step Functions.
Exposure to Databricks and vector databases (e.g., Pinecone, Weaviate, pgvector).
Knowledge of data mesh, data fabric, or graph-based architectures.

Why Join Us?

We value creative problem solvers who learn quickly, thrive in a diverse and open environment, and continuously push for higher standards. The team is focused on delivering high-quality solutions while maintaining a positive and engaging work culture.