Data Manager

Localpayment

. Lead, mentor, and grow the data team (data engineers and analysts), setting clear goals, career plans, and ongoing feedback.

Posted 11/3/2026full-timeRemote • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data teams and executing data strategies aligned with business objectives, while ensuring data quality and governance in compliance with regulations. Proficient in building scalable data platforms and pipelines, utilizing advanced data processing tools and technologies.

Highest-signal resume keywords

Data Team LeadershipData Platform DevelopmentAdvanced SQL ProficiencyCloud Data Stack ExperienceData Governance Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData AnalyticsData ModelingMachine Learning WorkflowsData Quality FrameworksPython for Data ProcessingSQLBI ToolsData PipelinesData Warehousing

Soft Skills

LeadershipCommunicationStakeholder ManagementStrategic ThinkingAdaptability

Tools & Technologies

AWSSnowflakeBigQueryAirflowDbtKafkaSQSLookerPower BITableau

Industry Keywords

FintechData GovernancePCI DSSData Protection RegulationsSelf-Service AnalyticsFraud DetectionRisk ScoringAI ToolsData QualityKPI Tracking

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSBigQueryCloudKafkaPythonSnowflakeSQLTableau

About the role

Key responsibilities & impact

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

What you’ll need

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.