Sporty's sites are some of the most popular on the internet, consistently staying in Alexa's list of top websites for the countries they operate in
As a Data Engineer at Sporty, you will play a critical role in ensuring the smooth processing and handling of data for our machine learning and data science initiatives. Your primary responsibilities will include designing, building, testing, optimising, and maintaining data pipelines and architectures for various aspects of our rapidly growing business.
Responsibilities
Design, develop and maintain scalable batch ETL and near-real-time data pipelines and architectures for various parts of our business, on fast and versatile data sources with millions of changes per day
Ensure all data provided is of the highest quality, accuracy, and consistency
Identify, design, and implement internal process improvements for optimising data delivery and re-designing infrastructure for greater scalability
Builds out new API integrations to support continuing increases in data volume and complexity
Communicate with data scientist, MLOps engineers, product owners and BI analysts in order to understand business processes and system architecture for specific product features
Requirements
Bachelor’s degree, or equivalent experience, in Computer Science, Engineering, Mathematics, or a related technical field
3+ years of experience in data engineering, data platforms, BI or related domain
Experience in successfully implementing data-centric applications, such as data warehouses, operational data stores, and data integration projects
Experience with large-scale production relational and NoSQL databases
Experience with data modelling
General understanding of data architectures and event-driven architectures
Proficient in SQL
Familiarity with one scripting language, preferably Python
Experience with Apache Airflow &Apache Spark
Solid understanding of cloud data services: AWS services such as S3, Athena, EC2, RedShift, EMR (Elastic MapReduce), EKS, RDS (Relational Database Services) and Lambda
Nice to have:
Understanding of ML Models
Understanding of containerisation and orchestration technologies like Docker/Kubernetes
Relevant knowledge or experience in the gaming industry
Benefits
Quarterly and flash bonuses
We have core hours of 10am-3pm in a local timezone, but flexible hours outside of this
Education allowance
Referral bonuses
28 days paid annual leave
2 x annual company retreats (Lisbon + Dubai in 2022 / Phuket in Q2 2023 + 1 more TBC!)
Highly talented, dependable co-workers in a global, multicultural organisation
Payment via world class online wallet system DEEL
Top of the line equipment supplied by market leader Hofy
We score 100% on The Joel Test
Our teams are small enough for you to be impactful
Our business is globally established and successful, offering stability and security to our Team Members
Interview Process
HackerRank Test
Remote video screening with our Talent Acquisition Team + live ID check
Remote 90 min video interview loop with 3 x Team Members (30 mins each)
Pre offer call with Talent Acquisition Team
ID check via Zinc
24-72 hour feedback loops throughout process
As a Data Engineer at Sporty, you will play a critical role in ensuring the smooth processing and handling of data for our machine learning and data science initiatives. Your primary responsibilities will include designing, building, testing, optimising, and maintaining data pipelines and architectures for various aspects of our rapidly growing business.
Responsibilities
Design, develop and maintain scalable batch ETL and near-real-time data pipelines and architectures for various parts of our business, on fast and versatile data sources with millions of changes per day
Ensure all data provided is of the highest quality, accuracy, and consistency
Identify, design, and implement internal process improvements for optimising data delivery and re-designing infrastructure for greater scalability
Builds out new API integrations to support continuing increases in data volume and complexity
Communicate with data scientist, MLOps engineers, product owners and BI analysts in order to understand business processes and system architecture for specific product features
Requirements
Bachelor’s degree, or equivalent experience, in Computer Science, Engineering, Mathematics, or a related technical field
3+ years of experience in data engineering, data platforms, BI or related domain
Experience in successfully implementing data-centric applications, such as data warehouses, operational data stores, and data integration projects
Experience with large-scale production relational and NoSQL databases
Experience with data modelling
General understanding of data architectures and event-driven architectures
Proficient in SQL
Familiarity with one scripting language, preferably Python
Experience with Apache Airflow &Apache Spark
Solid understanding of cloud data services: AWS services such as S3, Athena, EC2, RedShift, EMR (Elastic MapReduce), EKS, RDS (Relational Database Services) and Lambda
Nice to have:
Understanding of ML Models
Understanding of containerisation and orchestration technologies like Docker/Kubernetes
Relevant knowledge or experience in the gaming industry
Benefits
Quarterly and flash bonuses
We have core hours of 10am-3pm in a local timezone, but flexible hours outside of this
Education allowance
Referral bonuses
28 days paid annual leave
2 x annual company retreats (Lisbon + Dubai in 2022 / Phuket in Q2 2023 + 1 more TBC!)
Highly talented, dependable co-workers in a global, multicultural organisation
Payment via world class online wallet system DEEL
Top of the line equipment supplied by market leader Hofy
We score 100% on The Joel Test
Our teams are small enough for you to be impactful
Our business is globally established and successful, offering stability and security to our Team Members
Interview Process
HackerRank Test
Remote video screening with our Talent Acquisition Team + live ID check
Remote 90 min video interview loop with 3 x Team Members (30 mins each)
Pre offer call with Talent Acquisition Team
ID check via Zinc
24-72 hour feedback loops throughout process
Data source
We Work Remotely