Data Platform Lead

• Shape Platform Strategy & Architecture

• Pioneer Agentic AI & Analytics Innovation

• Modernize Pipelines & Event Streaming: Build, optimize, and manage highly resilient ETL/ELT pipelines using Snowflake, dbt, Airbyte, and Airflow. You will design modern ingestion and transformation architectures while enabling near-real-time event streaming capabilities through Kafka and similar technologies.

• Automate Workflows & Operational Processes: Leverage orchestration and automation platforms such as n8n to connect systems, streamline business operations, eliminate repetitive manual work, and enable scalable workflow automation across teams.

• Establish AI-Ready Data Foundations: Ensure all data products are discoverable, well-documented, governed, and trusted across the organization. You will establish strong data contracts, ownership models, observability standards, and governance practices so both humans and AI-powered systems can confidently work with data.

• Leverage Cloud & DevOps Infrastructure: Deploy and manage cloud-native data infrastructure on AWS while working with Infrastructure-as-Code and containerized environments using Terraform, Kubernetes, and GitLab CI/CD pipelines.

• Empower & Mentor a Skilled Team: Provide dedicated technical leadership and hands‑on mentorship to two data engineers. You will establish engineering standards, scalable operational practices, testing structures, and a strong culture of collaboration and technical excellence.

Requirements

• Proven Data Engineering Foundation: 5+ years of experience in data engineering or technical leadership, with strong expertise in SQL, Python, ETL/ELT, data modeling, and scalable data architectures.

• Modern Platform & Architecture Experience: Proven experience designing scalable data platforms and migrating legacy systems toward modular or event-driven architectures.

• Cross-Functional Communication: Strong experience collaborating with cross-functional stakeholders to align technical architecture with business goals.

• The Modern Data Stack: Hands-on expertise with Snowflake, dbt, Airflow, and Airbyte, plus experience with event-streaming technologies such as Kafka.

• Advanced Coding & DevOps: Strong Python skills and experience with AWS, Terraform, Kubernetes, GitLab CI/CD, and cloud-native infrastructure.

• Automation & AI Engineering: Experience or strong interest in analytics agents, LLM integrations, and workflow automation using tools such as OpenAI APIs, Anthropic APIs, n8n, LangChain, or LlamaIndex.

• Data Governance & Reliability: Strong understanding of data quality, governance, observability, security, and reliable data products.

Core Competencies

Demonstrates expertise in building and managing scalable data architectures, leveraging modern data stack technologies, and ensuring data governance and reliability. Proven ability to lead and mentor teams while aligning technical strategies with business objectives.

Highest-signal resume keywords

• Data Engineering

• SQL

• Python

• Snowflake

• AWS

ATS Optimization Keywords

Hard Skills

• ETL

• Data Modeling

• Scalable Data Architectures

• Event-Driven Architectures

• Data Governance

• Data Quality

• Observability

• Automation

• Analytics Agents

• Workflow Automation

Soft Skills

• Cross-Functional Communication

• Technical Leadership

• Mentorship

• Collaboration

• Problem-Solving

Industry Keywords

• Cloud-Native Infrastructure

• Data Products

• Event Streaming

• Operational Processes

• Data Contracts

Tools & Technologies

• Dbt

• Airflow

• Airbyte

• Kafka

• Terraform

• Kubernetes

• GitLab CI/CD

• N8n

• OpenAI APIs

• LangChain