Azure Data Engineer: Modern Etl Pipelines & Databricks

Capgemini Engineering; Quilmes; On-site; ARS 272,620,000 - 363,493,000; Part time

Benefits offered by this job

Professional growth

Competitive USD compensation and edu/健

Exciting projects

Flextime

Job summary

AgileEngine in Buenos Aires, Argentina seeks a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL pipeline decisions in an advisory capacity. You will mentor the team and review AI-assisted workflows to ensure engineering standards and governance.

The role focuses on EC2/ECS/ECR, Postgres RDS, S3, SageMaker pipelines, and data platform evolution, with 50% commitment rising to 25% as needed.

Qualifications

• 6+ years of hands-on AWS experience including ECS/Fargate, ECR, RDS, S3, SageMaker, VPC/ALB/CloudFront, IAM.
• Fluency in Infrastructure-as-code, specifically Terraform.
• Proven track record designing or auditing CI/CD pipelines and SDLC governance.
• Strong data engineering depth for ETL/ingestion architecture decisions.
• Experience evaluating AI-assisted development workflows (Claude Code) from engineering quality and cost governance perspective.
• Comfortable in a part-time advisory capacity (50% to 25%).

Responsibilities

• Audit AWS architecture across live applications (MAT / Signal IQ and the Impact Engine).
• Review CI/CD, end-to-end SDLC governance, and release gating.
• Provide early technical opinion on ETL/data ingestion architecture (managed vs. custom).
• Mentor and accelerate the infrastructure build team focusing on AWS, DevOps, CI/CD, and data engineering practices.
• Set out recommendations and technical direction from audit findings.
• Report findings to leadership with high-level risk framing.

Skills

AWS Expertise

Terraform

CI/CD pipelines

Data engineering

AI/LLM workflows

Mentoring

Education

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

Tools

Terraform

Job description

Data Engineering & Infrastructure Lead ID88014

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a part-time Data Engineering and Infrastructure Lead to audit AWS architecture, CI/CD processes, and ETL pipeline decisions in an advisory capacity. This person weighs managed tooling against custom builds, reviews SageMaker-based MLOps pipelines, and mentors the team on engineering standards. Evaluating AI-assisted development workflows is part of the role.

WHAT YOU WILL DO

• Audit the current AWS architecture across live applications (MAT / Signal IQ and the Impact Engine), including ECS/ECR, RDS (Postgres), S3, VPC, CloudFront/SSO, and SageMaker-based MLOps pipeline design.
• Review the CI/CD process and end-to-end app development lifecycle, recommending SDLC governance layers (schema versioning, environment separation, release gating).
• Evaluate how the team uses Claude Code (PR generation, automated reviews, token/cost management) to confirm output meets professional engineering standards.
• Provide an early technical opinion on ETL / data‑ingestion pipeline architecture (managed ELT tooling vs. custom build).
• Mentor and accelerate the day-to-day infrastructure build team, focusing on AWS, DevOps, CI/CD, and data engineering practices.
• Set out recommendations and technical direction where the audit surfaces necessary changes to the current build.
• Report findings and recommendations directly to leadership, framing high-level risk and readiness.

MUST HAVES

• 6+ years of deep, current hands‑on experience with AWS: ECS/Fargate, ECR, RDS (Postgres), S3, SageMaker, EventBridge, VPC/ALB/CloudFront, IAM.
• Fluency in Infrastructure-as-code, specifically Terraform.
• Proven track record designing or auditing CI/CD pipelines and SDLC governance (e.g., Alembic for schema/version control, environment separation, release gating).
• Strong data engineering depth to provide defensible opinions on ETL / ingestion architecture (managed tools vs. custom builds).
• Experience evaluating AI-assisted / LLM-assisted development workflows (e.g., Claude Code) from an engineering‑quality and cost‑governance standpoint.
• Comfortable operating in a part-time advisory capacity (50% stepping down to 25%) as a strong communicator who can mentor less experienced engineers.

NICE TO HAVES

• Prior experience working with small, fast-moving engineering teams.
• Understanding of MLOps pipelines and data science infrastructure.
• Media/AdTech domain knowledge is helpful but not required.

PERKS AND BENEFITS

• Professional growth : Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation : We match your ever‑growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects : Join projects with modern solutions development and top‑tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime : Tailor your schedule for an optimal work‑life balance, by having the options of working from home and going to the office – whatever makes you the happiest and productive.

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
• stablish 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
R

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.

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.

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 Corporation is one of the world’s leading integrated energy companies. Through its subsidiaries that conduct business worldwide, the company is involved in virtually every facet of the energy industry. Chevron explores for, produces and transports crude oil and natural gas; refines, markets and distributes transportation fuels and lubricants; manufactures and sells petrochemicals and additives; generates power; and develops and deploys technologies that enhance business value in every aspect of the company’s operations. Chevron is based in Houston, Texas. More information about Chevron is available at .

Chevron is an Equal Opportunity / Aff…