Data Engineering Consultant - Snowflake, United States - Hopmann Marketing Analytics GmbH

    Hopmann Marketing Analytics GmbH
    Hopmann Marketing Analytics GmbH Snowflake, United States

    1 month ago

    Default job background
    Full time
    Description
    (Senior) Data Engineering Consultant (m/f/d)

    Hopmann Marketing Analytics GmbH is a boutique consultancy for digital analytics, data engineering and data visualization based in Munich-Neuhausen.

    With our enthusiasm and determination, we support well-known clients from the telecommunications, automotive, IT, medical and retail sectors in analyzing their corporate data and work with them to develop action-oriented solutions that support data-driven decisions.

    Our passion is data and we want to make it accessible to everyone in meaningful visualizations and ad hoc reports.

    To support our team, we are now looking for a:
    (Senior) Data Engineering Consultant (m/f/d)

    Join our team

    WHEN
    Now

    HOW
    Full-time

    WHERE
    What we offer

    Working in an absolute trend profession in a professional environment
    From the beginning, exciting and varied projects with well-known clients from different industries
    Autonomy and participation in decision-making
    An open work atmosphere and flat hierarchies with competent colleagues
    Further training and certifications as well as participation in events and conferences
    Attractive pay and great additional benefits such as company pension schemes & discounts at the gym
    Regular team events that provide a balance to work
    Our state-of-the-art office in a great location with excellent connections
    1-month workation in Europe
    You support the design and implementation of data pipelines (ETL/ELT processes) and CI/CD pipelines using state-of-the-art technologies
    You create sophisticated data models with a focus on customizability and extensibility
    You maintain and expand existing cloud infrastructures and data warehouse systems
    You analyze and optimize existing data pipelines and infrastructures in terms of costs, runtime and stability
    You support the analysis and collection of customer requirements and the derivation of technical concepts from our customers' goals and strategies
    You advise our customers on the introduction and optimization of technologies from the "modern data stack"
    You work closely with our colleagues from the Data Visualization, Digital Analytics and Data Science teams to create innovative solutions
    You support your teammates professionally, technically and personally
    You create meaningful presentations and hold informative trainings and seminars
    Qualifications

    Completed studies, ideally with a technical background
    First professional experience, e.g. as a data engineer, data scientist or similar
    Very good knowledge of SQL and relational (cloud) databases (e.g. Snowflake, Redshift) and data modeling
    Very good knowledge of Python and corresponding libraries for data transformation and analysis (e.g. Pandas)
    Interest in cloud BI technologies (e.g. services from AWS, Azure, GCP)
    Interest in tools for data orchestration (e.g. Apache Airflow, Prefect) and data transformation (e.g. dbt)
    Interest in technologies and tools related to the "modern data stack", e.g. regarding data ingestion, testing & data quality, data lineage, reverse ETL
    Fluent in written and spoken German and English
    Knowledge of project management as well as very good analytical skills and strong communication skills
    As part of our team, we are looking for this position with immediate effect.

    We are passionate, committed and goal-oriented, can organize ourselves well, present ourselves confidently to our customers and want to bring the entire company forward as a team.

    Is analyzing complex matters also your strength, do you inspire others with your ideas and do you think you would fit in well with our team?
    We look forward to receiving your application with your earliest start date and salary expectations via e-mail to:

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