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Senior Data Engineer
hace 1 mes
Civitfun is opening a new data-focused vertical and is looking for a Senior Data Engineer with
approximately seven years of experience. This role will be responsible for designing and establishing the foundations of our data architecture, including the creation of a data warehouse on AWS and the definition of all associated data pipelines. The position involves close collaboration with the Platform team, the CTO, and development teams, with the goal of enabling data-driven decision-making across the company.
The ideal candidate combines strong technical skills with an understanding of business impact, and is able to translate business needs into robust, scalable, and cost-efficient data solutions.
Responsibilities:
- Design, build, and maintain scalable data pipelines and data models within the AWS ecosystem, ensuring data consistency, reliability, and performance.
- Lead the design and implementation of a new data warehouse, selecting appropriate components of the AWS Data Stack and defining the architectural principles that will guide future development.
- Understand the business domain and work closely with stakeholders to translate analytical needs into technical solutions that bring measurable business value.
- Anticipate data quality issues, bottlenecks, scalability challenges, and operational risks, proactively proposing improvements and preventive measures.
- Act as a technical reference within the data domain, collaborating with Platform, Development, and Business teams to align on priorities and deliverables.
- Promote best practices in data engineering, including data governance, testing, documentation, monitoring, and cost control.
- Review, analyse, and optimise data processes regularly, ensuring adherence to standards and continuous improvement across the data lifecycle.
- Participate in the design and planning of initiatives, contributing technical guidance and helping establish long-term data standards aligned with company goals.
- Stay updated on industry trends and emerging technologies and bring this knowledge into the team to improve processes, architecture, and tools
Technologies:
- Core work will rely on the AWS Data Stack. This may include, but is not limited to, services such as Amazon Redshift, AWS Glue, Amazon S3, AWS Lambda, Athena, Step Functions, and related AWS analytics services.
- Experience with Snowflake, Tableau, or other enterprise-grade analytics and reporting tools is considered a plus.
- Familiarity with modern data engineering tooling, orchestration frameworks, and CI/CD workflows is valued.
Requirements:
- Approximately seven years of experience in data engineering, data management, or similar roles, preferably within cloud-based environments.
- Strong understanding of data modelling, ETL/ELT processes, and distributed data systems.
- Experience working with AWS or another major cloud provider in a data-focused context.
- Ability to evaluate and design solutions based on business impact, scalability, reliability, and cost efficiency.
- Strong analytical, organisational, and time-management skills.
- Excellent communication skills, with the ability to work effectively across technical and non-technical teams.
- Ability to operate with autonomy, lead initiatives end-to-end, and act as a technical reference for others.
Day-to-Day:
- Work closely with the Platform team, CTO, and developers to define data priorities, design data processes, and integrate new data sources into the data platform.
- Collaborate with business stakeholders to understand reporting and analytical needs and translate them into data models, datasets, and pipelines.
- Design, test, and deploy data workflows using AWS components, ensuring scalability, reliability, and cost control.
- Participate in planning sessions, reviews, and other agile ceremonies, contributing technical expertise and aligning on deliverables.
- Continuously refine and improve data pipelines, monitoring systems, and architectural components based on performance, business needs, and industry best practices.
- Ensure that data is consistently available, high quality, and ready for analysis, supporting the organisation’s transition toward data-driven decision making.
