Data Engineering Lead

Job Summary:

We're seeking an experienced Data Engineering Practice Lead with expertise in AI/ML and GenAI to lead our data engineering practice, driving the design, development, and delivery of scalable, secure, and efficient data solutions. The ideal candidate will have a strong technical background, excellent leadership skills, and experience in data engineering, architecture, and management.

Key Responsibilities:

1. Practice Leadership: Lead the data engineering practice, setting direction and vision for the team, with a focus on AI/ML and GenAI.

2. Technical Architecture: Design and implement scalable, secure, and efficient data architectures for data related workloads.

3. Data Engineering: Develop and refine data engineering processes, ensuring best practices and industry standards.

4. Team Management: Mentor and manage a team of data engineers, providing guidance and support on data projects.

5. Project Management: Oversee data engineering projects, ensuring timely delivery and high quality.

6. Client Engagement: Collaborate with clients to understand their data needs and provide solutions.

7. Technical Research: Stay up-to-date with industry trends, researching new technologies and innovations in AI/ML and GenAI.

8. Process Improvement: Continuously improve data engineering processes, streamlining workflows and increasing efficiency for data related projects.

9. Collaboration: Work with cross-functional teams, including data science, analytics, and IT, to integrate AI/ML and GenAI solutions.

10. Knowledge Sharing: Develop and share knowledge resources, best practices, and thought leadership.

Requirements:

1. Technical Expertise: 8+ years of experience in data engineering, architecture, or related fields, with a focus on AI/ML and GenAI.

2. Leadership Experience: 3+ years of experience leading technical teams or practices.

3. AI/ML Experience: Experience with AI/ML technologies, such as TensorFlow, PyTorch, or scikit-learn.

4. GenAI Experience: Experience with GenAI technologies, such as language models or computer vision.

5. Data Engineering Skills: Proficient in data engineering tools, technologies, and methodologies (e.g., Apache Beam, Spark, Kafka, Hadoop).

6. Data Architecture: Experienced in designing and implementing data architectures (e.g., data lakes, data warehouses) for AI/ML and GenAI workloads.

7. Programming Skills: Strong programming skills in languages like Python, Java, or Scala, with experience in AI/ML and GenAI frameworks.

8. Cloud Computing: Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) and their data offerings.

9. Communication Skills: Excellent communication and interpersonal skills.

10. Project Management: Experience with project management methodologies (e.g., Agile, Waterfall).

Nice to Have:

1. Certifications: Data engineering or related certifications (e.g., Certified Data Engineer, Certified Analytics Professional).

2. Industry Knowledge: Experience in specific industries (e.g., finance, healthcare, retail).

3. Research Experience: Experience in research and development of AI/ML and GenAI technologies

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