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описание
Merkle is an integrated experience consultancy with a heritage in data science and business performance. It delivers end-to-end experiences that drive growth, engagement, and loyalty, combining expertise in digital transformation, commerce, experience design, engineering, digital marketing, data science, CRM, and customer data management.
задачи
Design and implement data ingestion and processing for various data sources using public cloud services
Work with stakeholders on data-related technical issues and data infrastructure needs, including optimizing data delivery and redesigning infrastructure for scalability
Collaborate with Business Intelligence consultants to assemble complex data sets that meet functional and non-functional business requirements for data lakehouses
Support machine learning teams with the deployment and optimization of AI and Machine Learning models and other data algorithms
Develop data pipelines that provide actionable insights into marketing automation, customer acquisition, and other key business areas
Automate release pipelines using infrastructure as code and CI/CD tools
Document data pipelines and logic in Confluence and plan activities using Agile methodology in Jira
Support pre-sales by proposing technical solutions and providing effort estimates
требования
Experience building and productionizing big data architectures, pipelines, and data sets
Understanding of big data concepts and patterns, data lakes, lambda architecture, stream processing, DWH, and BI
3–7 Years of experience in a Data Engineer role
Advanced Python programming skills
Experience with object-oriented, functional, or scripting languages such as Bash, Scala, Java, R, or PowerShell
Experience with data services in Azure, AWS, and GCP public clouds
Experience with big data technologies such as Databricks, Fabricm, AWS Glue, Snowflake, Dataproc, or BigQuery
Extensive experience with relational databases such as MS SQL, Postgres, and Aurora DB, and NoSQL databases such as DynamoDB, MongoDB, Elasticsearch, and Redis
Experience with streaming services such as Kafka, Event Hubs, and Kinesis
Experience with orchestration, compute, and ETL services such as dbt, Airflow, Cloud Composer, AWS Step Functions, AWS Lambda, and Azure Functions
Strong analytical skills working with structured and unstructured datasets
Ability to build processes for data transformation, data structures, metadata, dependency management, and workload management
Experience setting up and using CI/CD automation tools such as Azure DevOps, GitHub Actions, and Jenkins
Precision, strong organization, good communication, adaptability, and accountability for own work
Будет плюсом: delivering business intelligence projects using Power BI, Tableau, or Looker, understanding web-tracking frameworks such as Adobe Analytics and Google Analytics, experience with Salesforce Cloud services
условия
Hybrid work models, adaptable hours, and home office options
Competitive salaries, volunteer days, wellness days, modern offices, and a multicultural environment
Training, mentorship, and data-driven projects; opportunities across the dentsu network
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