Data Manager

Job not on LinkedIn

🔥 21 hours ago

🇨🇴 Colombia – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Localpayment

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2018

💳 Fintech

🔌 API

🤝 B2B

Fintech • API • B2B

Localpayment is a payments infrastructure company that provides a single API to power secure, seamless cross-border payments in emerging markets. Part of Aleph Group, Localpayment offers pay-in and pay-out capabilities, treasury and FX services, virtual accounts, on/off ramps for crypto, fraud protection, smart routing and multi-currency conversion. It targets businesses (advertisers, digital platforms, fintechs and marketplaces) with a strong Latin America footprint and a roadmap for global expansion, enabling high success rates and local bank coverage to scale cross-border commerce.

📋 Description

• Lead, mentor, and grow the data team (data engineers and analysts), setting clear goals, career plans, and ongoing feedback. • Define and execute the data strategy and roadmap in alignment with Product, Engineering, Finance, and Operations. • Own the design and evolution of the data platform: ingestion, modeling, warehousing, and serving layers on the cloud. • Build and maintain reliable, scalable data pipelines (batch and streaming) that process high volumes of payment transactions across multiple countries. • Ensure data quality, lineage, and observability, defining SLAs and monitoring for critical datasets. • Establish data governance, access control, and security practices aligned with PCI DSS and local data-protection regulations. • Deliver trusted reporting and self-service analytics for key business areas: transactions, settlements, reconciliation, treasury, merchant performance, and revenue. • Define and track company-wide KPIs and metrics, acting as the single source of truth for business data. • Partner with Risk, Compliance, and Product teams on fraud detection, risk scoring, and ML/AI use cases. • Drive the adoption of AI tools across the data lifecycle to accelerate analysis, automation, and decision-making. • Translate business questions into data solutions and communicate insights clearly to leadership and non-technical stakeholders. • Support hiring, onboarding, and the professional development of data talent.

🎯 Requirements

• 6–8+ years of experience in data engineering, analytics, or BI, with at least 2–3 years leading data teams. • Proven experience building and scaling data platforms and teams in fast-growing tech companies or fintechs. • 4+ within a Fintech environment. • Advanced SQL and strong proficiency in Python for data processing and automation. • Hands-on experience with cloud data stacks, preferably AWS (S3, Redshift, Glue, Athena, Lambda) or equivalents such as Snowflake or BigQuery. • Experience with orchestration and transformation tools such as Airflow and dbt, and with streaming/messaging technologies such as Kafka or SQS. • Solid knowledge of data modeling (dimensional modeling, data warehouse / lakehouse architectures). • Experience with BI and visualization tools such as Looker, Power BI, Tableau, or Metabase. • Strong understanding of data governance, data quality frameworks, and security best practices in regulated environments. • Exposure to machine learning workflows (feature engineering, model deployment, fraud/risk models) is a plus. • Active, day-to-day use of AI tools (e.g., Claude, Copilot, or similar) in data work. • Fluent in English and Spanish. • Strong leadership, communication, and stakeholder management skills with a high ownership mindset. • Strategic thinking — ability to connect data initiatives with business outcomes and prioritize impact. • Ability to translate complex data concepts into clear insights for non-technical audiences. • Adaptability and comfort working in fast-paced, multicultural, and distributed environments.

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