Tech Lead, Data Engineering

Responsibilities

Architect and evolve a scalable, cost-efficient AWS data platform that enables enterprise analytics, reporting, and future AI capabilities

Design and implement reliable, production-grade data pipelines and processing frameworks for batch and near real-time data

Define enterprise standards for data architecture, modeling, observability, reliability, and platform performance

Lead foundational technical decisions across tooling, infrastructure, data design, scalability, and operational efficiency

Implement and maintain infrastructure-as-code, CI/CD pipelines, and automated deployment practices across the data platform

Enable high-quality, accessible, and governed data products through scalable semantic layers, curated datasets, and transformation standards

Provide hands-on technical leadership, mentoring, and architectural direction for a growing team of data engineers and cross-functional stakeholders

Capabilities

Ability to design scalable, secure, and cost-efficient data architectures that support enterprise analytics and future AI initiatives

Strong technical problem-solving and decision-making skills, including balancing tradeoffs between scalability, complexity, performance, and cost

Ability to establish engineering standards, operational best practices, and reliable platform governance processes

Strong understanding of CI/CD pipelines, modern software engineering practices, and production support models

Experience enabling accessible, high-quality data products for analytics, reporting, and downstream business consumers

Ability to work collaboratively across technical and business stakeholders while providing hands-on technical leadership and direction

Exposure to streaming technologies (e.g., Kafka or Kinesis), transformation frameworks such as dbt, multi-cloud environments, and/or ML and data science workflows is beneficial

Qualifications

6+ years of experience in data engineering with strong expertise in scalable system and platform design

Proven experience building, modernizing, or significantly evolving enterprise data platforms and architectures

Deep hands-on experience within the AWS data ecosystem, including production-scale data lake or lakehouse environments utilizing technologies such as S3, Glue, Spark, Athena, and/or Redshift

Strong proficiency in Python and SQL with experience developing production-grade data pipelines and transformation workflows

Experience implementing workflow orchestration solutions, preferably Airflow

Experience with infrastructure-as-code and automated deployment practices using tools such as Terraform or AWS CDK

Familiarity with modern data warehousing and lakehouse concepts, including dimensional modeling and platforms such as Snowflake, Redshift, or Databricks