- அனுபவம்
- 3+ ஆண்டுகள்
- சம்பளம்
- —
- காலியிடங்கள்
- 1
- பதிவுசெய்யப்பட்டது
- 7 மணி நேரம் முன்
- வேலை முறை
- வீட்டிலிருந்து வேலை
- சுயவிவரம்
- விண்ணப்பிக்க வேண்டும்
பணி விளக்கம்
Position Overview
A partner company is seeking a Founding Data Engineer to join their technology team remotely from Germany. This role is a remarkable chance to architect and build the foundational elements of a modern data ecosystem that supports analytics, business intelligence, and upcoming AI projects. The engineer will be responsible for designing and scaling a data platform, influencing architectural decisions, engineering norms, and data methodologies in collaboration with leadership and cross-disciplinary teams.
Key Responsibilities
- Design, develop, and maintain scalable ELT/ETL pipelines integrating data from products, payment gateways, and external APIs.
- Build and oversee cloud data warehouses using platforms like Snowflake, BigQuery, or Redshift.
- Develop and manage data workflow orchestration through Airflow, Prefect, Dagster, or similar tools.
- Optimize data warehouse efficiency including performance, scalability, and cost through robust modeling and query optimization techniques.
- Create maintainable, testable data transformation workflows with dbt or equivalent frameworks.
- Implement comprehensive data quality checks, monitoring, lineage tracking, and documentation procedures.
- Establish secure and privacy-conscious data handling policies, including access management and sensitive data protection.
- Design semantic layers and unified business metrics to support analytics and organizational decision-making.
- Collaborate with Product, Growth, and Engineering teams on analytics, reporting, experimentation, event tracking, and API integrations.
- Champion DataOps practices including CI/CD, version control, automated testing, and documentation standards.
- Contribute to the ongoing evolution of data strategy and foundational technologies for future AI and machine learning efforts.
Qualifications and Experience
- Minimum 3 years’ experience in data engineering or a similar role focused on data systems.
- Advanced proficiency in SQL and Python; Scala knowledge is an advantage.
- Hands-on experience with cloud-based data warehouses such as Snowflake, BigQuery, or Redshift.
- Proven track record with production data pipelines and scalable architectures.
- Experience with orchestration tools including Airflow, Prefect, or Dagster.
- Strong grasp of data modeling and transformation using dbt or comparable tools.
- Competence in implementing data quality, monitoring, governance, and documentation.
- Excellent communication skills to effectively collaborate with diverse teams.
- A strong sense of ownership focused on reliability and maintainability over time.
- Familiarity with secure data handling and privacy-focused practices.
Desired Qualifications
- Experience in B2C SaaS, subscription services, or marketplace business environments.
- Knowledge of product analytics platforms like Segment, Amplitude, or Mixpanel.
- Exposure to creating semantic layers or canonical data models.
- Experience with streaming technologies such as Kafka or Kinesis.
- Involvement with ML infrastructure, feature stores, or AI-centric data systems.
- Previous experience in startup or scale-up environments building data platforms.
Benefits and Work Conditions
- Competitive salary package.
- Fully remote work with flexible hours.
- 22 paid leave days plus local public holidays.
- Opportunity to design and influence a state-of-the-art data platform from inception.
- High degree of ownership and impact on architecture and engineering practices.
- Access to contemporary tools and modern engineering workflows.
- Chance to tackle meaningful technical challenges with business impact.
- A collaborative, product-driven culture where data is central to decision-making.
- Professional growth opportunities in a dynamic tech environment.
திறன்கள்
SQL
பைதான் நிரலாக்கம்
Data Pipeline Development
Data quality monitoring
Cloud data warehouses (Snowflake, BigQuery, Redshift)
Data workflow orchestration (Airflow, Prefect, Dagster)
Data modeling and transformation (dbt)
Privacy and data security practices
Collaboration and communication skills
DataOps methodologies
Optimization of data systems