Data Engineering Lead

Join Our Next Generation Enterprise Data Platform TeamDesign, build, and lead the evolution of Coates' next-generation enterprise data platform on AWS, establishing scalable and secure foundations for how data is integrated, governed, transformed, and consumed across the organization. This role enables business intelligence, analytics, operational decision-making, and future AI capabilities through hands-on technical leadership, architecture design, and cross-functional partnership.Responsibilities:Architect and evolve a scalable, cost-efficient AWS data platform that enables enterprise analytics, reporting, and future AI capabilitiesDesign and implement reliable, production-grade data pipelines and processing frameworks for batch and near real-time dataDefine enterprise standards for data architecture, modeling, observability, reliability, and platform performanceLead foundational technical decisions across tooling, infrastructure, data design, scalability, and operational efficiencyImplement and maintain infrastructure-as-code, CI/CD pipelines, and automated deployment practices across the data platformEnable high-quality, accessible, and governed data products through scalable semantic layers, curated datasets, and transformation standardsProvide hands-on technical leadership, mentoring, and architectural direction for a growing team of data engineers and cross-functional stakeholdersCapabilities:Ability to design scalable, secure, and cost-efficient data architectures that support enterprise analytics and future AI initiativesStrong technical problem-solving and decision-making skills, including balancing tradeoffs between scalability, complexity, performance, and costAbility to establish engineering standards, operational best practices, and reliable platform governance processesStrong understanding of CI/CD pipelines, modern software engineering practices, and production support modelsExperience enabling accessible, high-quality data products for analytics, reporting, and downstream business consumersAbility to work collaboratively across technical and business stakeholders while providing hands-on technical leadership and directionExposure to streaming technologies (e.g., Kafka or Kinesis), transformation frameworks such as dbt, multi-cloud environments, and/or ML and data science workflows is beneficialQualifications:5+ years of experience in data engineering with strong expertise in scalable system and platform designProven experience building, modernizing, or significantly evolving enterprise data platforms and architecturesDeep 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 RedshiftStrong proficiency in Python and SQL with experience developing production-grade data pipelines and transformation workflowsExperience implementing workflow orchestration solutions, preferably AirflowExperience with infrastructure-as-code and automated deployment practices using tools such as Terraform or AWS CDKFamiliarity with modern data warehousing and lakehouse concepts, including dimensional modeling and platforms such as Snowflake, Redshift, or Databricks$145,200 - $165,000 a year Full Salary Range: $145,200/year (minimum), $155,100/year (midpoint), $165,000/year (maximum). Pay is based on relevant experience, skills, education, internal equity, and market data. Well-qualified candidates can generally expect offers around the midpoint. Candidates who meet the minimum qualifications but have more limited directly relevant experience for this specific role are typically placed nearer the minimum, while highly experienced candidates with strong role alignment may be placed closer to the maximum.