Germany, Germany
May 9, 2023
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At intive we innovate through design and engineering to create people-centric digital products. We are more than 3000 people with a passion for digital, who partner with the world’s leading brands such as Dow Jones, FOX, Warner Bros., Verizon, Macmillan, Facebook, Google, BMW, BlackBerry, Discovery, and more.
Our clients come to us to solve their biggest and the most complex business and technology challenges. We have over 20 years of experience working with companies across many industries, including Media & Entertainment, EdTech, Telecommunications, Retail, Automotive, FinTech, and Transportation.
What You Will Be Doing
Our clients come to us to solve their biggest and the most complex business and technology challenges. We have over 20 years of experience working with companies across many industries, including Media & Entertainment, EdTech, Telecommunications, Retail, Automotive, FinTech, and Transportation.
What You Will Be Doing
- Designing, building, and maintaining scalable data pipelines and ETL processes for customer profile, behavior and e-commerce data
- Collaborating with data scientists and analysts to ensure accurate and timely data delivery
- Integrating and harmonizing data from various sources, including DWH, MDM, and various Frontend channels into a CDP solution
- Ensuring data quality, integrity, and consistency throughout the data lifecycle
- Staying up-to-date with industry best practices and emerging technologies to optimize e-commerce data engineering processes
- Supporting our Product Owners and consultants with data-related technical decisions and insights
- At least 5 years of experience in data engineering, with at least 3 years focused on e-commerce projects
- A strong foundation in programming languages like Python, Java, or Scala
- Hands-on experience with popular Customer Data Platforms (CDPs) such as Segment, Tealium, or Adobe RTCDP
- Proficiency in SQL and NoSQL databases such as Azure SQL Database, Cassandra, MongoDB, and HBase
- Understanding the concepts of data warehousing, including ETL processes and data modeling
- Experience in integrating data from various sources, such as web services, APIs, and file systems
- Proficiency in building data pipelines using tools such as AWS Data Pipeline, Azure Data Factory, Apache NiFi, Kafka, Airflow or similar
- A deep understanding of the data lifecycle and data quality best practices
- Knowledge in big data technologies, such as Hadoop and Spark
- Experience working with cloud platforms, such as AWS, Azure, or GCP
- Comfort in working in an international (remote) team
- Good communication skills, problem-solving skills, and a willingness to learn are essential to succeed as a data engineer
- Being skilled in data visualization tools, such as Tableau or PowerBI
- Experienced working with machine learning frameworks and libraries
