Data Engineering Manager Architect - Chicago - Net2Source (N2S)

    Net2Source (N2S)
    Net2Source (N2S) Chicago

    17 hours ago

    Technology / Internet
    Description

    Role: Data Engineering Manager/Architect

    Type: Full-Time Direct Hire

    Location: Chicago IL/Onsite

    Education: Bachelor's degree in a relevant field.

    Years of Experience: Minimum of 12 years in relevant experience, preferably with a consulting background and experience with Pharma clients.

    Role Description

    1. We are looking for passionate individuals to help our clients solve complex challenges, enabling their sustained analytics transformation.
    2. As a member of our Client Services team, you will lead a team of Associates and Senior Associates who design, execute, and implement cutting-edge analytical solutions.
    3. Responsible for institutionalizing data-driven insights and recommendations to bring customer strategies to life.
    4. Manage client engagements and relationships while leading internal teams, generating insights, and developing recommendations.
    5. Become familiar with the strategic direction of our customers, helping them translate their goals into action on existing projects while also developing a pipeline of additional projects.

    Job Responsibilities

    As a Data Architect - Customer Success Manager, you will:

    • Design and define scalable data architecture frameworks, including data models, data flows, and platform structures aligned with business needs.
    • Lead architectural discussions and provide guidance on building, modernizing, or migrating data platforms to cloud and big data ecosystems.
    • Collaborate with cross-functional teams (Data Engineering, Data Science, Product, and Business) to translate complex requirements into robust data solutions.
    • Address substandard work or work that does not meet the firm's or clients' expectations.
    • Establish best practices for data modelling, data governance, data quality, and data lifecycle management.
    • Develop a point of view on key global trends and their impact on clients.
    • Manage various viewpoints to build consensus and create positive outcomes for all parties.
    • Identify risks, gaps, and inefficiencies in existing data systems and recommend optimization strategies
    • Simplify complex messages, highlighting and summarizing key points.
    • Uphold organizational standards, compliance requirements, and ethical practices in all data architecture and solution design initiatives.

    Skills Required

    • 10+ years of experience in designing and implementing distributed applications.
    • Proven ability to carry out complex Proof of Concept (POC), pilot projects, and limited production rollouts, focusing on developing new or improved techniques and procedures.
    • Strong experience in data architecture design, including scalable data models, data frameworks, and end-to-end data ecosystem planning.
    • Expertise in data modelling, data warehousing, and schema design (conceptual, logical, and physical models).
    • Solid understanding of data pipelines and ETL/ELT architecture, including design, optimization, and governance (not just implementation).
    • Experience participating in architectural discussions and guiding cloud migration or modernization of data platforms.
    • Proficiency in SQL and strong knowledge of data integration, transformation strategies, and data lifecycle management.
    • Experience with Big Data and cloud ecosystems (e.g., EMR, Airflow, Spark, Hive, Hadoop) from an architecture and solutioning perspective.
    • Familiarity with performance optimization, data pipeline scalability, and query tuning at an architectural level.
    • Ability to collaborate with cross-functional stakeholders (Engineering, Data Science, Product, and Business teams) to define data solutions aligned with business needs.
    • Experience working in Agile delivery environments for data and analytics programs.
    • Strong understanding of engineering best practices, including version control, documentation, and scalable design standards.
    • Exposure to PySpark/Spark and modern data platforms, with a focus on solution design rather than pure hands-on development.
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