Starte jetzt als Lead Cloud Data Engineer bei Saras Analytics in Bayern

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