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Senior Data Engineer
dentsu AustriaData Engineer designing and building big data architectures for cloud-based data lake houses at Merkle. Collaborating with international teams on data projects for leading brands.
Tech Stack
Tools & technologiesAirflowAWSAzureBigQueryCloudDynamoDBElasticSearchETLGoogle Cloud PlatformJavaJenkinsKafkaMongoDBNoSQLPostgresPythonRedisScalaSQL
About the role
Key responsibilities & impact- Design and implement data ingestion and processing of various data sources using public cloud services.
- Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs, like optimizing existing data delivery, re-designing infrastructure for greater scalability, etc.
- Collaborate with Business Intelligence consultants and assemble large and complex data sets that meet functional and non-functional business requirements for data lakehouse.
- Support machine learning teams in deployment and optimization of AI / Machine Learning models and other data algorithms.
- Develop data pipelines to provide actionable insights into marketing automation, customer acquisition and other key businesses areas.
- Use infrastructure as a code and CI/CD tools to automate release automation pipelines.
- Document data pipelines and logic in a structured manner using Confluence, plan your activities using Agile methodology in Jira.
- Support pre-sales by proposing technical solutions and accurate effort estimation.
Requirements
What you’ll need- Experience in building and productionizing big data architectures, pipelines and data sets.
- Understanding data concepts and patterns of big data, data lake, lambda architecture, stream processing, DWH, and BI.
- 3-7 years of experience in a Data Engineer role, who has attained experience using the following software/tools: Advanced Python programming.
- Experience with other object-oriented / functional / scripting languages like Bash, Scala, Java, R, PowerShell etc.
- Data related services from Azure, AWS, and GCP public clouds.
- Big data technologies like Databricks, Fabricm, AWS Glue, Snowflake, Dataproc, BigQuery, etc.
- Extensive working experience with relational databases like MS SQL, Postgres, Aurora DB and NoSQL databases like DynamoDB, MongoDB, Elasticsearch, Redis, etc.
- Streaming technology services like Kafka, Event Hubs, and Kinesis, etc.
- Orchestration, compute, and ETL services like dbt, Airflow, Cloud Composer, AWS Step Functions, AWS Lambda, Azure Functions etc.
- Strong analytic skills related to working with structured and unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- Experience in setting up and using CI/CD automation tools like Azure DevOps, GitHub actions, Jenkins, etc.
- Person who is precise, well organized, has good communication skills, can adapt to changing circumstances and is not afraid of responsibility for his / her work will do great in this role.
Benefits
Comp & perks- Flexibility & Balance: Enjoy hybrid work models, adaptable hours, and home office options.
- Inclusive & Supportive Culture: We celebrate diversity and foster a respectful workplace where every team member’s unique perspective is valued, and everyone is welcome.
- Well-balanced reward package: We offer competitive salaries, volunteer days, wellness days, modern offices, and a multi-cultural environment that fosters creativity and innovation.
- Innovation-Driven Work: Contribute to projects that are reshaping industries and driving transformation with next-generation technologies and strategies.
- Purpose-Driven Culture: Be part of an organization that fosters sustainable growth for brands, people, and society—where every contribution makes a meaningful impact.
- Growth Opportunities: Expand your skills through training, mentorship, and data-driven projects that reshape customer experience and business outcomes.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Data IngestionData ProcessingBig Data ArchitectureMachine Learning DeploymentData Pipeline DevelopmentData TransformationStreaming TechnologyETL ServicesData AnalysisInfrastructure as Code
Soft Skills
Good Communication SkillsOrganizational SkillsAdaptabilityResponsibility