Senior Ml Data Engineer: Cost Analytics & Pipelines

Caramel Point; Quilmes; On-site; ARS 1,800,000 - 2,800,000; Full time

Benefits offered by this job

Relocation support

Job summary

Chevron Global Business Services (GBS) in Buenos Aires, Argentina, seeks a senior Data Engineer to own data pipelines, governance, and platform delivery. You will partner with ML engineers and product teams to enable AI-ready data and scalable analytics.

Role emphasizes Python/SQL development, Databricks, and Azure cloud services. You will lead regional data initiatives, ensure data quality, and drive end-to-end data engineering excellence.

Qualifications

• BS in Computer Science, MIS, Computer Engineering or related field.
• +5 years in Data Engineer role with data platform ownership.
• Experience with Azure cloud data services and Databricks.
• Knowledge of data governance, lineage, and data quality practices.

Responsibilities

• Develop and maintain data pipelines and ETL processes.
• Build and operate a production data platform with scalable pipelines.
• Collaborate with ML engineers to deploy models and data services.
• Define data models, contracts, and SLOs; ensure data quality.

Skills

Python

SQL

APIs

ETL

Data pipelines

Cloud computing

NLP/ML integration

Education

Bachelor's degree in CS/IS/CE

Tools

Azure Data Factory

Databricks

Azure Synapse

GIT

Azure ML

Python libraries for data science

Job description

We are seeking a Machine Learning Engineer with strong expertise in Python and a focus on Natural Language Processing (NLP) to join our team. The ideal candidate will have experience in the finance or fintech domain, specifically in reconciliation and accounting.

Responsibilities

• Develop and implement machine learning models for document understanding and processing.
• Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and analysis.
• Apply NLP techniques using libraries like spaCy, NLTK, or transformers.
• Implement fuzzy matching algorithms (Levenshtein distance, record linkage) for data accuracy.
• Process documents using OCR, PDF parsing, and Excel data extraction methods.
• Deploy machine learning models as REST APIs using Docker and Azure ML.
• Understand and apply supervised and unsupervised learning techniques.
• Design and manage data pipelines and ETL processes.
• Collaborate with cross-functional teams to meet project goals.

Required Skills

• Strong expertise in Python (Pandas, NumPy, scikit-learn).
• Experience with NLP libraries (spaCy, NLTK, transformers).
• Knowledge of fuzzy matching algorithms.
• Document processing skills (OCR, PDF parsing, Excel extraction).
• Experience in ML model deployment (REST APIs, Docker, Azure ML).
• Understanding of supervised/unsupervised learning techniques.
• Familiarity with deep learning frameworks (TensorFlow, PyTorch).
• Experience with Large Language Models (LLMs) for document understanding (OpenAI, Azure OpenAI).
• Knowledge of Azure ML services and Azure Cognitive Services.
• 2-5 years of experience in ML engineering, focusing on NLP or financial data processing.
• Experience in the finance/fintech domain (reconciliation, accounting).

Education

Master’s degree in Computer Science, Data Science, or a related field.

The Executive Director, Data Engineering is the functional leader for network data within Global Network Services, directly leading a 25 person engineering organization focuses on Network Data. Data is the foundation layer of the platforms we build: automation, observability, analytics, and AI all depend on trustworthy, queryable network state. This leader owns the network data platform end to end, from sourcing and modelling through to the serving layer that engineering teams consume, and sets the strategy for how network data is governed, delivered, and continuously improved at scale.

In parallel, the Executive Director serves as the Argentina (Buenos Aires) Regional Lead for Infrastructure Platform Foundational Services (Network, Storage, DataCentre, Data Protection and Recovery). Accountable for site strategy, talent development, governance, executive presence, and operational alignment across the region. The role establishes Argentina as a strategic engineering hub by building and sustaining high-performing engineering teams aligned to global priorities and outcomes.

This leader partners closely with the automation platform, SRE, and Product Engineering teams, and with firm-wide data governance and data quality programmes, to ensure network data is well governed and fit for production use.

Key Responsibilities

Functional Responsibilities - Data Engineering

• Define and execute the network data strategy, covering data modelling, authoritative sources, distribution, and how data is made available to consuming platforms
• Build and operate the data platform: ingestion and streaming pipelines, transformation, storage and modelling, and performant serving APIs and self-service access for engineering teams
• Establish consistent data models and identity standards for network entities (device, circuit, site, interface) so data can be reliably joined and reused across systems
• Drive data quality as an engineering discipline: validation, automated testing, monitoring, and measurable quality targets across the data lifecycle
• establish data contracts and data SLOs covering freshness, completeness, accuracy, and availability, with clear ownership, versioning, and schema-evolution standards
• Implement lineage and observability across data pipelines so issues can be traced to source and change impact can be assessed ahead of time
• Partner with firm-wide data governance and data quality programmes to meet audit, risk, and regulatory obligations
• Make network data AI-ready: structured, well-described, and accessible so agentic and LLM-driven capabilities can reason over accurate state, and apply AI within the data lifecycle itself
• Establish engineering standards, testing frameworks, and CI/CD pipelines so data pipelines and services are production-grade, secure-by-design, and observable

Deliver measurable outcomes: data SLO attainment, improved data quality metrics, faster access to network data, and demonstrable downstream gains in automation, reliability, and analytics

Regional Responsibilities - Argentina IP Foundational Services Site Lead

• Represent the Argentina hub in global Infrastructure Platforms Foundational Services forums.
• Drive site culture, retention, and consistent engineering standards across functional silos
• Ensure effective cross-functional collaboration with security, infrastructure, application teams, service management, and business partners to deliver integrated outcomes
• Manage regional resource planning and budget inputs: capacity forecasting, skills coverage, on-call sustainability, and investment recommendations tied to measurable service improvements
• Hire, develop, and retain engineering talent across the Argentina footprint

Ensure governance, risk, and audit compliance for in-country operations

• Strong ownership of data platform and data quality outcomes
• Ability to build and scale engineering teams as a manager-of-managers across functional and matrixed reporting lines
• Drives a product-led approach to data, with clear ownership, published standards, and consumers treated as customers
• Strong executive communication skills, including the ability to make the business case for foundational data investment
• Maintains compliance and control discipline

Credible senior technology presence in-region, able to represent JPMC externally with regulators, universities, and partners

Required Qualifications, Capabilities, and Skills

• 10+ years in data engineering or data platform engineering, including leadership roles running data systems at scale and manager-of-managers experience
• Proven ownership of a production data platform: pipelines, modelling, storage, and serving, with accountability for freshness, correctness, and availability
• Demonstrated experience establishing data governance in practice: ownership models, data contracts, data quality measurement, lineage, and schema evolution
• Strong hands‑on engineering foundation (Python, SQL, APIs, streaming and batch pipelines, modern data stores) and the judgment to hold a high engineering bar
• Proven track record adopting agentic AI and LLM-driven capability in production engineering environments, and preparing data estates for AI consumption
• Experience operating within formal risk and control frameworks: audit engagement, evidence quality standards, and remediation governance
• Proven ability to scale teams and drive adoption of common data standards across partner teams
• Strong global collaboration skills across distributed, matrixed organizations
• Prior site, country, or regional engineering hub leadership experience

BS/BA degree or equivalent practical experience in technology, engineering, or a related discipline

Preferred Qualifications, Capabilities, and Skills

• Infrastructure or network domain experience: inventory, topology, configuration, and telemetry data. A strong plus, not a gate
• Experience with source‑of‑truth platforms, or graph and topology data models
• Experience with streaming telemetry and time‑series data at scale
• Financial services or other regulated environment experience
• Experience supporting automation and reliability engineering teams as primary data consumers
• Demonstrated use of AI to redesign data engineering workflows for measurable impact, and to build organizational AI fluency
• Demonstrated experience in incident, problem, and change governance, including executive communications during high‑severity events

Team Scope

• Direct Org: 25 engineers (network data engineering), globally distributed
• Growth aligned to the Network Services data platform build‑out
• Partnership: strong partnership with the automation platform and SRE teams, global Product Engineering, firm‑wide data governance and data quality programmes, and regional Infrastructure leadership
• Regional (matrix): site leadership for all IP Foundational Services engineers in Argentina

Buenos Aires, Buenos Aires, Argentina

Full time

Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.

Job Description

GBS Chevron Global Business Services (GBS), located in Buenos Aires (Puerto Madero), Argentina, is accepting applications for the position of Data Engineer. Successful candidates will join the IT Organization, which is part of a multifunction service and technical center with a workforce of more than 1800 employees that deliver business services and solutions to the corporation across the globe.

Responsibilities include:

• Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
• Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
• Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
• Responsible for normalizing and ingesting data from multiple sources.
• Enable data scientists, making data available for advanced analytics models, and contributing to models.
• Work with ML Engineer to scale and deploy solution including model, documentation, training, integration
• Contributing to the inner source development of foundational tools, and/or the deployment of technical services.

Required Qualifications:

• BS in Computer Science, Management Information Systems, Computer Engineer or related fields or equivalent experience.
• +5 years in Data Engineer Role
• Knowledge and/or experience with: data acquisition, preparation and validation; data movement and transformation; analytics solution architecture; big data computing; cloud computing, core data architecture; information security technologies and software engineering using technologies such as Azure Data Factory (ETL), Databricks, DevOps, CI/CD Pipeline Deployment, Python, Java, Ansible, Azure SQL, Azure Synapse, GIT, ADO, Azure Data Lake, Azure Analysis Services, Logic Apps, Microsoft Data Flows, Kimball Data Warehousing Methodology
• Analytical thinking, Consulting, Critical Thinking

Relocation Options: Relocation may be considered.

International Considerations: Expatriate assignments will not be considered.

Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time‑limited Visa status for this position.

Chevron participates in E‑Verify in certain locations as required by law.

Chevron is an Equal Opportunity / Aff… employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity… or protected veteran status, or other status protected by law or regulation.

Argentina Full‑time