11 publicaciones de empleo destacadas de sr. ai security engineer en remote (¡Están contratando!)

As a Data Engineer, you will be responsible for building and maintaining scalable, reliable, and secure data platforms that power analytics and AI use cases across the organization.

This role is critical in enabling data-driven decision-making in financial services environments. You will work closely with data scientists, AI engineers, and business stakeholders to ensure data systems are robust, performant, and aligned with regulatory and operational requirements.

Experience Bands

Senior Data Engineer: 8–10 years of experience
Data Engineer: 5–7 years of experience

Key Responsibilities

Design and implement robust ETL/ELT pipelines for structured and unstructured data
Build and manage scalable data lakes, data warehouses, and real-time data pipelines
Ensure data quality, lineage, governance, and compliance across data platforms
Enable reliable data availability for analytics, reporting, and AI systems
Optimize data infrastructure for performance, scalability, and cost efficiency
Collaborate with Data Science and AI teams to productionize machine learning pipelines
Monitor and troubleshoot data workflows and system performance
Implement best practices for data security and reliability

Financial Services Use Cases (Preferred)

Candidates with experience in financial data environments will be highly valued, particularly in:

Transaction data pipeline development and management
Regulatory reporting and compliance data systems
Risk and finance data marts
Customer 360 and customer analytics platforms

Technical Skills

Data Platforms

Snowflake
BigQuery
Amazon Redshift
Databricks

Data Processing Technologies

Apache Spark
Apache Kafka
Apache Flink

Databases

SQL databases
NoSQL databases

DevOps & Engineering Practices

CI/CD pipelines
Version control systems (e.g., Git)

Containers & Infrastructure

Docker
Kubernetes

Cloud Platforms

AWS
Azure
Google Cloud Platform (GCP)

Evaluation Criteria

Candidates will be evaluated based on:

Complexity and scale of data systems built and maintained
Reliability and performance of data pipelines in production environments
Experience implementing data governance and compliance standards
Exposure to AI and machine learning data pipelines
Ability to design scalable and resilient data architectures

Key Performance Indicators (KPIs)

Reliability of data pipelines (uptime, failure rate)
Data latency and freshness
Data quality and integrity metrics
Cost optimization and efficiency of data infrastructure
Stability and scalability of data platforms

Preferred Profile

Experience working within financial data ecosystems
Understanding of regulatory data requirements and compliance standards
Exposure to MLOps and machine learning data pipelines
Experience working in distributed or cross-functional teams
Strong problem-solving and ownership mindset

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Role Overview

Experience Bands

Key Responsibilities

Financial Services Use Cases (Preferred)

Technical Skills

Evaluation Criteria

Key Performance Indicators (KPIs)

Preferred Profile

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