Вакансия Data Engineering Team Lead

Skills

• AWS
• AWS Glue
• Amazon Redshift
• Amazon S3
• AWS Kinesis
• OpenSearch
• Amazon QuickSight
• Tableau
• Microsoft Power BI
• Athena
• ETL
• ELT
• Apache Spark
• PySpark
• Terraform
• AWS SageMaker
• Amazon Bedrock
• Python

Описание вакансии

Automat-it is where high-growth startups turn when they need to move faster, scale smarter, and make the most of the cloud. As an AWS Premier Partner and Strategic Partner, we deliver hands-on DevOps, FinOps, and GenAI support that drives real results.
We work across EMEA, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.
We’re looking for a Data Engineering Team Lead to build and scale our Data & Analytics capability while delivering modern, production-grade data platforms for customers on AWS. You’ll lead a team of Data Engineers, own delivery quality and timelines, and remain hands-on across architecture, pipelines, and analytics so the team ships fast, safely, and cost-effectively.

Responsibilities

• Manage, coach, and grow a team of Data Engineers through 1:1s, goal setting, feedback, and career development.
• Own end-to-end delivery outcomes (scope, timelines, quality) across multiple projects; unblock the team and ensure on-time, high-quality releases.
• Lead customer-facing workshops, discovery sessions, and proof-of-concepts, serving as the primary technical point of contact to translate requirements into clear roadmaps, estimates, and trade-offs in plain language.
• Support solution proposals, estimates, and statements of work; contribute to thought leadership and reusable accelerators.
• Collaborate closely with adjacent teams (MLOps, DevOps, Data Science, Application Engineering) to ship integrated solutions.
• Design, develop, and deploy AWS-based data and analytics solutions to meet customer requirements. Ensure architectures are highly available, scalable, and cost-efficient.
• Develop dashboards and analytics reports using Amazon QuickSight or equivalent BI tools.
• Migrate and modernize existing data workflows to AWS. Re-architect legacy ETL pipelines to AWS Glue and move on-premises data systems to Amazon OpenSearch/Redshift for improved scalability and insights.
• Build and manage multi-modal data lakes and data warehouses for analytics and AI. Integrate structured and unstructured data on AWS (e.g. S3, Redshift) to enable advanced analytics and generative AI model training using tools like SageMaker.

Requirements

• Proven leadership experience with a track record of managing and developing technical teams.
• Production experience with AWS cloud and data services, including building solutions at scale with tools like AWS Glue, Amazon Redshift, Amazon S3, Amazon Kinesis, Amazon OpenSearch Service, etc.
• Skilled in AWS analytics and dashboards tools – hands-on expertise with services such as Amazon QuickSight or other BI tools (Tableau, Power BI) and Amazon Athena.
• Experience with ETL pipelines – ability to build ETL/ELT workflows (using AWS Glue, Spark, Python, SQL).
• Experience with data warehousing and data lakes - ability to design and optimize data lakes (on S3), Amazon Redshift for data warehousing, and Amazon OpenSearch for log/search analytics.
• Proficiency in programming (Python/PySpark) and SQL skills for data processing and analysis.
• Understanding of cloud security and data governance best practices (encryption, IAM, data privacy).
• Excellent communication and customer-facing skills with an ability to explain complex data concepts in clear terms. Comfortable working directly with clients and guiding technical discussions.
• Fluent written and verbal communication skills in English.
• Proven ability to lead end-to-end technical engagements and work effectively in fast-paced, Agile environments.
• AWS certification – AWS certifications, especially in Data Analytics or Machine Learning are a plus.
• DevOps/MLOps knowledge – experience with Infrastructure as Code (Terraform), CI/CD pipelines, containerization, and AWS AI/ML services (SageMaker, Bedrock) is a plus.