Senior Data Engineer
Job description
About the role
You will define and implement the data architecture that powers our client's modern analytics and machine learning initiatives. This role centers on designing robust pipelines that transform complex operational data into trusted assets. You will own the end to end data flow, ensuring scalability, performance, and reliability across the platform. Collaboration with business stakeholders will be central as you translate ambiguous requirements into concrete data models. You will play a key role in moving the organization beyond legacy BI toward a cloud native lakehouse paradigm. Your work will directly influence how insights are generated and consumed across the business. You will mentor junior engineers and contribute to technical standards that shape our data culture. This position is ideal for a hands on builder who wants to leave a lasting architectural impact.
Key facts
What you'll do
Architect and deliver scalable data pipelines that ingest, process, and serve operational data for analytics and reporting.
Design and implement modern ETL and ELT workflows using cloud native patterns to ensure high quality, resilient, and maintainable data flows.
Lead the design of a scalable data warehouse and lakehouse architecture on cloud platforms in partnership with Databricks.
Partner with business stakeholders to define reporting requirements, metrics, and data needs that drive strategic decisions.
Establish data quality frameworks and governance standards to improve consistency, reliability, and trust in data assets.
Analyze existing data collection gaps and propose enhancements to event tracking, schemas, and data models.
Build analytics ready datasets that enable self service exploration and rapid insight generation for end users.
Develop dimensional and semantic data models that support advanced analytics, machine learning, and customer facing products.
Champion data engineering best practices, including documentation, testing, monitoring, and infrastructure as code.
Collaborate with data scientists and BI developers to prepare datasets that support predictive models and embedded analytics.
Evaluate and integrate emerging tools and patterns to keep the data platform aligned with industry innovation.
Drive automation of data workflows to reduce manual effort and improve operational efficiency.
Act as a technical leader in data platform discussions, influencing standards and long term vision.
Mentor team members and foster a culture of knowledge sharing, learning, and continuous improvement.
Requirements
You must possess a strong commercial background in Data Engineering with multiple years delivering data platforms in production.
You have hands on experience designing, building, and operating modern data pipelines and ETL or ELT processes.
You have worked with data warehouses or modern lakehouse architectures, understanding modeling techniques for large datasets.
Your SQL skills are advanced, and you can write complex queries to transform and aggregate large volumes of data.
You have practical experience with cloud based data platforms, with a demonstrated background using Databricks.
You have been part of building or significantly evolving data platforms from the ground up in a growing environment.
You understand data modeling, data quality, governance, and data architecture best practices at a deep level.
You can communicate effectively with business stakeholders and translate ambiguous problems into scalable data solutions.
You have a track record of enabling Business Intelligence through clean, well structured, and analytics ready datasets.
You have experience preparing data for advanced analytics and machine learning use cases, including feature engineering considerations.
You approach problems analytically, identifying opportunities for improvement in data collection, pipelines, and reporting.
You communicate clearly and collaborate proactively, thriving in distributed team environments across time zones.
You are comfortable making technical decisions and documenting the rationale for architectural choices.
You take ownership of end to end delivery, ensuring that solutions are reliable, performant, and maintainable.
Nice to have
Experience with BI tools such as Power BI, Tableau, or Looker.
Experience designing customer facing analytics or embedded reporting solutions.
Experience working in fast growing product or technology companies.
Practical notes
This is a 100% remote position.
A monthly WFH allowance is provided as financial support for remote working.
The engagement is based on standard working hours, and availability during core business hours is expected.
Candidates must be located in LATAM due to current project and client constraints.
Visa sponsorship is not available for this role.
There is no relocation support or equipment provision from the company.
Travel is not required for this role.
The position does not include specific deadlines for application submission beyond standard hiring timelines.
About the company and mission
Zartis is a global AI transformation and technology consulting partner where talented engineers and technologists work on cutting edge innovation. We partner with ambitious organizations to design, build, and scale technology solutions that deliver real impact. Our teams bring deep expertise in AI driven platforms, secure API architectures, and cloud native engineering. You will work on meaningful projects that accelerate the adoption of advanced technologies, from strategy and discovery through to full product delivery, helping turn complex challenges into measurable outcomes. With engineering hubs across EMEA and LATAM, and long term partnerships in financial services, healthcare and life sciences, and energy and climate, we offer opportunities to work on projects that truly matter. Here, you will not just build technology, you will drive business impact and grow your career alongside industry leaders.
What you will do
Design, build, and maintain scalable data pipelines to ingest, transform, and process operational data.
Develop modern ETL/ELT workflows that deliver reliable, high-quality datasets.
Help design a scalable data warehouse/lakehouse architecture using modern cloud technologies.
Work closely with business stakeholders to understand reporting and analytics requirements.
Improve data quality, consistency, governance, and documentation across the platform.
Identify gaps in existing data collection and recommend improvements to tracking and data models.
Enable self-service analytics by delivering clean, well-structured datasets for downstream BI tools.
Prepare data models that support advanced analytics, machine learning, and customer-facing insights.
Contribute to establishing data engineering best practices, standards, and scalable architecture.
Requirements
Strong commercial experience in Data Engineering.
Proven experience designing and building modern data pipelines and ETL/ELT solutions.
Experience working with data warehouses or modern lakehouse architectures.
Strong SQL skills and experience modeling large datasets.
Experience with cloud-based data platforms, ideally Databricks.
Experience building or evolving data platforms from the ground up.
Strong understanding of data modeling, data quality, governance, and data architecture best practices.
Ability to work directly with business stakeholders to translate business problems into scalable data solutions.
Nice to have
Experience with BI tools such as Power BI, Tableau, or Looker.
Experience designing customer-facing analytics or embedded reporting solutions.
Experience working in fast-growing product or technology companies.