Lead Cloud Data Engineer in Bayern bei Saras Analytics

Lead Cloud Data Engineer

About Saras Analytics

We are an ecommerce focused end to end data analytics firm assisting enterprises & brands in data driven decision making to maximize business value. Our suite of work spans extraction, transformation, visualization & analysis of data delivered via industry leading products, solutions & services. Our flagship product is Daton, an ETL tool. We have now ventured into building exciting ease of use data visualization solutions on top of Daton. And lastly, we have a world class data team which understands the story the numbers are telling and articulates the same to CXOs thereby creating value.

Where we are Today

We are a boot strapped, profitable & fast growing (2x y-o-y) startup with old school value systems. We play in a very exciting space which is intersection of data analytics & ecommerce both of which are game changers. Today, the global economy faces headwinds forcing companies to downsize, outsource & offshore creating strong tail winds for us. We are an employee first company valuing talent & encouraging talent and live by those values at all stages of our work without comprising on the value we create for our customers. We strive to make Saras a career and not a job for talented folks who have chosen to work with us.

The Role

We are seeking a highly accomplished and visionary Lead Cloud Data Engineer to lead our data engineering efforts, shape our data infrastructure strategy, and drive innovation within our organization. As a Lead Cloud Data Engineer, you will leverage your extensive experience to design, build, and optimize complex data solutions in the cloud. You will lead a team of data engineers, collaborate with cross-functional teams, and play a pivotal role in transforming our data landscape. If you are a dynamic leader with a strong background in data engineering, cloud technologies, and programming excellence, we invite you to apply for this leadership position.

Responsibilities

• 
  Data Strategy: Develop and execute a forward-thinking data strategy, encompassing data architecture, technology stack, and best practices to support business goals.
• Team Leadership: Lead and mentor a team of data engineers, fostering a culture of innovation, collaboration, and continuous learning.
• Data Pipeline Excellence: Oversee the design, development, and maintenance of enterprise-level data pipelines, ensuring scalability, reliability, and performance.
• Advanced Data Transformation: Utilize your expertise in Python and/or PySpark to architect and implement complex data transformations, optimizations, and ETL processes.
• Data Governance and Compliance: Establish and enforce data governance standards, ensuring data quality, security, lineage, and compliance with industry regulations.
• Cross-Functional Collaboration: Collaborate closely with data scientists, analysts, and stakeholders to understand complex data requirements and deliver tailored data solutions.
• Documentation and Best Practices: Promote best practices in data engineering and maintain comprehensive documentation of data pipelines and processes.
• Security and Scalability: Implement robust security measures and strategies for data scalability within cloud environments.

Preferred Skills

• 
  DBT (Data Build Tool): Extensive experience with DBT for orchestrating and managing data transformations.
• Containerization and Orchestration: Proficiency in containerization and orchestration tools (e.g., Docker, Kubernetes) for efficient deployment and scalability.
• Data Streaming: Knowledge of data streaming technologies (e.g., Kafka, Apache Spark Streaming) for real-time data processing.
• Workflow Management: Familiarity with data orchestration and workflow management tools (e.g., Apache Airflow) for scheduling and automation.
• Cloud Certification: Relevant cloud certifications (e.g., AWS Certified Data Analytics, Azure Data Engineer) demonstrating your expertise in cloud platforms.

Qualifications

• Bachelor’s degree in computer science, Information Technology, or a related field (master’s degree preferred).
• Minimum 8 years of relevant experience.
• Extensive experience in data engineering, with a strong focus on cloud-based solutions.
• Proven leadership experience, including managing and mentoring data engineering teams.
• Expertise in Python and/or PySpark for advanced data processing, transformation, and ETL tasks.
• Deep understanding of cloud platforms such as AWS, Azure, or Google Cloud.
• Advanced knowledge of data warehousing concepts and technologie