2 months ago
Prague, CzechiaMid Level / Senior
Responsibilities
- Design and implement data ingestion and processing for varied data sources using public cloud services.
- Build and optimize big data architectures, data lakehouse pipelines, datasets, and data delivery infrastructure.
- Assemble large and complex datasets that meet functional and non-functional business requirements.
- Support machine-learning teams with deployment and optimization of AI and machine-learning models and data algorithms.
- Develop data pipelines supporting marketing automation, customer acquisition, and other business areas.
- Build internal platform frameworks for consistent and optimized delivery across technologies, teams, solutions, and clients.
- Automate release pipelines using infrastructure-as-code and CI/CD tools.
- Document data pipelines and logic in Confluence and plan activities in Jira.
- Support stakeholders with data-related technical issues and infrastructure needs.
- Support pre-sales by proposing technical solutions and producing effort estimates.
Requirements
- 3–7 years of experience in a Data Engineer role.
- Experience building and productionizing big data architectures, pipelines, and datasets.
- Understanding of big data concepts and patterns, data lakes, lambda architecture, stream processing, data warehouses, and business intelligence.
- Advanced Python programming skills.
- Experience with object-oriented, functional, or scripting languages such as Bash, Scala, Java, R, or PowerShell.
- Experience with data services across Azure, AWS, and GCP.
- Experience with big data technologies such as Databricks, Fabricm, AWS Glue, Snowflake, Dataproc, and BigQuery.
- Extensive experience with relational databases such as MS SQL, PostgreSQL, and Aurora DB, and NoSQL databases such as DynamoDB, MongoDB, Elasticsearch, and Redis.
- Experience with streaming technologies 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.
- Experience building 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.
- Preferred experience delivering business intelligence projects using Power BI, Tableau, or Looker.
- Preferred understanding of web-tracking frameworks such as Adobe Analytics and Google Analytics.
- Preferred experience with Salesforce Cloud services.
- Strong organization, communication, adaptability, accountability, and collaboration skills.
Benefits
- Permanent, full-time employment.
- Hybrid work models, adaptable hours, and home-office options.
- Competitive salary, volunteer days, wellness days, modern offices, and a multicultural environment.
- Training, mentorship, and opportunities to work on data-driven international projects.
- Global collaboration across Merkle and the dentsu network.
- Inclusive and supportive workplace culture with a stated commitment to diversity, equity, and inclusion.
Tech Stack
Amazon DynamoDBApache AirflowApache KafkaAWSAzureBashDatabricksdbtElasticsearchGitHub ActionsGoogle BigQueryGoogle Cloud PlatformJavaJenkinsMongoDBPostgreSQLPowerShellPythonRRedisScalaSnowflake
Categories
Data Engineering
About Dentsu
Dentsu is a global advertising and marketing services group that plans and buys media, develops creative campaigns, and builds data- and technology-led customer experiences for brands. It earns revenue from agency fees and media services across a network that includes Carat, iProspect, and Merkle. Founded in 1901 and headquartered in Tokyo, Dentsu is publicly traded in Japan and serves clients across many sectors worldwide.