Data Platform Engineer

Inter Miami CF LLC•Fort Lauderdale, FL

About The Position

Inter Miami CF is building a world-class football organization driven by innovation, performance, and data-informed decision making. As the club continues to grow on and off the pitch, our Analytics department plays a critical role in delivering insights that support coaching, scouting, sports science, and player health. We are seeking a Data Platform Engineer to design and operate the core data infrastructure that powers football analytics across the club. This role will lead the development of a scalable cloud data platform that ingests information from tracking systems, performance technologies, and football analytics providers, transforming raw data into reliable datasets used by technical staff and decision-makers throughout the organization. The ideal candidate combines strong engineering expertise with a passion for building data systems that impact on-field performance. You will work closely with analysts, performance staff, and coaches to ensure that the club’s data ecosystem is reliable, efficient, and capable of supporting advanced football intelligence.

Requirements

5+ years of professional Python development, building production-grade data systems.
3+ years of hands-on experience working with AWS cloud infrastructure, including Lambda, S3, ECS, Glue, Athena, SQS, IAM, and CloudWatch.
Strong SQL skills and experience developing analytics data models using dbt or similar tools.
Experience designing and operating ETL pipelines and data platforms in cloud environments.
Experience managing infrastructure with Terraform or equivalent Infrastructure-as-Code tooling.
Experience with workflow orchestration platforms such as Prefect, Airflow, or Dagster.
Familiarity with containerized environments using Docker.
Experience implementing data quality, validation, and schema management practices.
Strong software engineering practices including version control, automated testing, and CI/CD.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.

Nice To Haves

Experience working with sports analytics data, particularly football/soccer.
Familiarity with major football data providers such as StatsBomb, Tracab, StatsSports, VALD, or Sportec Solutions.
Experience working with large JSON datasets and streaming parsers such as ijson.
Experience with columnar data formats such as Parquet and libraries such as PyArrow.
Observability experience using monitoring tools such as Grafana or DataDog.
AWS certification such as Solutions Architect.
Spanish language proficiency.

Responsibilities

Design, implement, and maintain serverless data pipelines on AWS that ingest and process football analytics data from multiple external providers.
Build and operate an event-driven data architecture using S3, SQS, Lambda, and workflow orchestration to reliably process incoming datasets.
Develop robust ETL pipelines in Python that standardize, validate, and transform raw tracking, event, and performance data into structured analytical datasets.
Implement data validation and schema enforcement using tools such as Pandera to maintain consistent and trustworthy data across systems.
Maintain and optimize the AWS data lake architecture, including Glue Data Catalog definitions, table partitioning, and efficient storage formats.
Develop and maintain dbt transformation models that produce curated datasets for analytics, scouting, sports science, and medical teams.
Optimize Athena queries and data partitioning strategies to support fast and cost-efficient analytics workflows.
Ensure the platform supports advanced football analysis including match events, player tracking, performance monitoring, and workload metrics.
Manage the club’s analytics infrastructure in AWS using Terraform infrastructure-as-code.
Deploy and maintain containerized workloads with Docker and ECS Fargate for data processing services.
Build and maintain CI/CD pipelines with GitHub Actions to support automated testing, deployment, and infrastructure updates.
Monitor pipeline health, troubleshoot failures, and manage message queues and dead-letter systems to ensure operational reliability.
Implement lifecycle management and cost controls for large sports datasets stored in S3.
Serve as the technical lead for analytics engineering, helping guide architectural decisions and development practices.
Collaborate closely with coaching staff, scouts, sports scientists, and analysts to understand data needs and deliver reliable data products.
Support onboarding of new data providers and technologies within the club’s analytics ecosystem.
Maintain technical documentation, architectural diagrams, and operational runbooks for long-term platform sustainability.
Help manage vendor relationships, analytics tooling, and technology budgets within the department.