What makes you a great fit:

Technical Understanding of Data Engineering

• Data Pipeline Architecture: Knowledge of data pipelines (ETL/ELT) and how data moves from source to destination.
• Cloud Platforms: Familiarity with platforms like AWS, Azure, or Google Cloud for data storage, processing, and analytics. Databases & Querying: Understanding different types of databases (SQL, NoSQL) and data storage systems (e.g., Data Lakes, Warehouses).
• Big Data Frameworks: Experience with big data technologies such as Hadoop, Spark, or Kafka.
• APIs & Integrations: Understanding how APIs and various data integrations work between systems.
• Data Security & Compliance: Awareness of regulations such as GDPR, HIPAA, and practices around data privacy and security.

Project Management Expertise

• Agile & Scrum Methodologies: Familiarity with Agile frameworks, sprint planning, backlog grooming, and release management.
• Stakeholder Management: The ability to manage expectations of cross-functional teams (data scientists, data engineers, business leaders).
• Risk Management: Proactively identifying, assessing, and mitigating project risks related to timelines, resources, and dependencies.
• Budget & Resource Planning: Managing budgets, resource allocation, and timelines for efficient project delivery.
• Documentation & Reporting: Efficient tracking and documentation of project progress, challenges, and solutions, keeping stakeholders informed.

Data Governance & Quality Management

• Data Governance Frameworks: Understanding data governance practices such as role definition (Data Owner, Custodian) and compliance mechanisms.
• Data Quality: Ensuring accuracy, consistency, and reliability of data, and managing processes for data validation and cleansing.
• Metadata Management: Knowledge of tools and practices to manage and document metadata across systems.

Cross-functional Leadership

• Collaboration with Engineers: Strong ability to work closely with data engineers, architects, and developers, understanding their challenges and providing clear guidance.
• Business Understanding: Ability to translate business needs into technical specifications and ensure alignment with business objectives.
• Communication: Clear and effective communication skills for presenting updates, risks, and strategies to both technical and non-technical stakeholders.

Analytical and Problem-Solving Skills

• Problem-Solving: The ability to quickly analyze issues within data flows, integrations, or processes and facilitate resolution.
• Decision-Making: Making informed decisions based on data-driven insights, balancing between technical constraints and business goals.
• KPIs and Metrics: Familiarity with key performance indicators for project success, including data processing speed, reliability, and accuracy.

Tool Proficiency

• Project Management Tools: Proficiency in tools like Jira, Trello, or Asana for project tracking.
• Data Engineering Tools: Awareness of data tools like Apache NiFi, Talend, or Informatica for managing data flows.
• Version Control & CI/CD: Basic understanding of version control systems (e.g., Git) and Continuous Integration/Continuous Deployment pipelines.

Adaptability and Continuous Learning

• Staying Updated: Ability to stay current with emerging trends in data engineering, cloud computing, and big data technologies.
• Adaptability: Flexibility to adjust project plans or technical approaches as new information or challenges arise.