Data Lead

Data Lead

About Promtior

Promtior is at the forefront of the Agentic AI revolution, delivering cutting-edge solutions that transform businesses across industries — bridging technology and people to turn AI into a real competitive advantage. Since 2023, we've helped organizations boost operational efficiency through tailored Agentic AI solutions, from discovery and development to full implementation, across three core lines: AI Product Delivery (predictive analytics, intelligent automation, AI-driven chatbots, image processing), AI Department as a Service (LATAM-based teams that integrate directly with our clients' teams), and AI Adoption Consulting (helping companies find and act on AI opportunities across their business).

We're a team held together by genuine relationships, constant exchange, and shared learning. We believe innovation happens when people feel included and collaborate with purpose, and we aim for professional growth to go hand in hand with personal well-being.

About the Role

We're looking for a Data Lead to head our Data team within Engineering. This is a hands-on leadership role: you will set the technical direction for data engineering and data science work across client projects, grow the team, and stay close enough to the code to review designs and unblock production issues.

Key Responsibilities

You will lead Promtior's Data team — data engineers and data scientists — defining its technical vision, standards, and ways of working across client engagements. You will own the architecture of the data platforms we design and deliver, from ingestion and Lakehouse / Warehouse design (Databricks, Snowflake, or equivalent) to the curated datasets that feed analytics, reporting, and AI/ML use cases. You will set the team's engineering standards for data modeling, testing, CI/CD, documentation, data quality, observability, and governance, and make sure they hold in production. You will make and defend pragmatic architectural decisions, balancing performance, reliability, cost, and time to value, and explain those trade-offs to both technical and business audiences.

You will partner with clients, project managers, and AI/ML teams to translate business problems into data solutions, scope new initiatives, and participate in pre-sales and discovery when data expertise is needed. You will oversee delivery quality across projects, lead the resolution of critical production incidents, and drive root cause analysis to prevent recurrence. You will hire, mentor, and develop the team through code and design reviews, technical guidance, and career development, and you will help shape how we use AI tools to accelerate data discovery, development, and documentation.

Required Experience

• 5+ years of experience in data engineering, data architecture, or data science, including at least 3 years leading a data team or setting technical direction for other engineers.

• Proven track record designing and operating data platforms in production, owning them end to end, from design to post-deployment operation.

• Advanced experience with at least one modern data platform (Databricks or Snowflake) and Lakehouse / Medallion architectures (Bronze/Silver/Gold).

• Advanced Python and SQL for large-scale data processing.

• Strong command of relational and analytical data modeling and Data Warehousing.

• Experience designing ETL/ELT and incremental pipelines with orchestration and transformation tools (Airflow, dbt, or similar).

• Experience defining standards for data quality, lineage, observability, CI/CD, and testing applied to data.

• Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP).

• Working knowledge of the ML lifecycle and experience supporting data for AI/ML or LLM-based use cases in production.

• Experience hiring, mentoring, and reviewing the work of data engineers or data scientists.

• Ability to communicate architecture, trade-offs, and risks to clients and non-technical stakeholders.

• Advanced English Level.

Especially Valued

• Experience in a consulting or services company, leading data work across several clients or projects at once.

• Having built a data platform or a data team from an early stage, rather than only maintaining an established one.

• Experience with streaming and near real-time processing (Kafka, Spark Structured Streaming).

• Experience with MLOps tooling (MLflow, feature stores, model registries).

• Experience with Infrastructure as Code (Terraform) applied to data platforms.

• Experience optimizing cost and performance of large-scale data platforms (FinOps).

• Experience modernizing legacy environments (e.g., SQL Server) toward cloud data platforms.

• Experience in regulated or high-volume environments (fintech, payments, insurance).

• Experience working directly with U.S. clients.

• Official certifications (Databricks, Snowflake, AWS, Azure, GCP, or equivalent).

Benefits

• 20 paid days off per year.

• Hybrid work model.

• Flex Days: monthly team activities.

• Professional development courses.

• Internal clubs (soccer, padel, running, cinema).

Location

Montevideo, Uruguay.

Buenos Aires, Córdoba, Corrientes or Chaco, Argentina.

· Hybrid work arrangement