Remote Ml Engineer: Data Analytics & Impact

Muttdata; Rosario; Remote; ARS 2,000,000 - 3,200,000; Full time

Benefits offered by this job

Competitive salary package

Career coaching & development

Learning opportunities & training

Hybrid work & flexible hours

Wellbeing programs

Global tech communities & inclusion

Job summary

Endava is seeking a senior Data Engineer to join our data team and help transform raw data into actionable insights. You will design, build, and maintain scalable ETL/ELT pipelines, implement Airflow workflows, and work with Snowflake and DBT to ensure data quality and reliability across cloud environments.

The role emphasizes production-grade data pipelines, infrastructure-as-code practices, and collaboration with architecture, DevOps, product, and analytics teams to drive data-driven decision

Qualifications

• 5+ years of professional experience in data engineering or a related software engineering role.
• Strong hands-on experience building and supporting production-grade data pipelines.
• Strong knowledge of Apache Airflow, including DAG development, scheduling, dependencies, retries, and monitoring.
• Strong experience with Snowflake or equivalent cloud data warehouse technologies.
• Proven experience with DBT.
• Solid experience with AWS or another major cloud platform.
• Experience with CI/CD and Infrastructure-as-Code practices.
• Strong programming skills in Python and SQL.
• Demonstrated experience troubleshooting complex production issues and performing root-cause analysis.
• Ability to independently own complex technical initiatives from design through implementation and production.
• Strong communication, problem-solving, and technical documentation skills.

Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.
• Develop reusable, modular, and configuration-driven data processing solutions.
• Contribute to technical architecture and evaluate solutions for scalability, performance, reliability, and maintainability.
• Design and optimize data models, SQL queries, stored procedures, and data processing workflows.
• Design and maintain Apache Airflow workflows, including scheduling, dependencies, retries, monitoring, and error handling.
• Develop automated data validation and quality checks using DBT and SQL.
• Implement monitoring, alerting, and observability for critical data pipelines.
• Develop data solutions using AWS and cloud-based data warehouse technologies such as Snowflake.
• Work with cloud services for data storage, processing, compute, messaging, and serverless workloads.
• Support data platforms across development, QA, staging, and production environments.
• Optimize data workloads for performance, scalability, reliability, and cost.
• Develop and maintain CI/CD pipelines for data engineering applications and infrastructure.
• Apply Infrastructure-as-Code practices using Terraform or equivalent technologies.
• Support production deployments, incident resolution, root-cause analysis, and continuous improvement.
• Maintain technical documentation, operational runbooks, and troubleshooting guides.
• Take ownership of complex engineering initiatives from design through production.
• Conduct code reviews and promote engineering best practices.
• Mentor junior and mid-level engineers and provide technical guidance.

Skills

Python

SQL

Airflow

DBT

PySpark

Snowflake

AWS

AWS S3

EMR

Lambda

SNS/SQS

GitHub Actions

Jenkins

Tools

GitHub

Airflow

Job description

Company Description

Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world‑class engineering, industry expertise and a people‑centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses. From prototype to real‑world impact – be part of a global shift by doing work that matters.

Job Description

Our data team has expertise across engineering, analysis, architecture, modeling, machine learning, artificial intelligence, and data science. This discipline is responsible for transforming raw data into actionable insights, building robust data infrastructures, and enabling data‑driven decision‑making and innovation through advanced analytics and predictive modeling. As a Data Engineer at Endava you will:

• Data Engineering & Architecture
• Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.
• Develop reusable, modular, and configuration‑driven data processing solutions.
• Contribute to technical architecture and evaluate solutions based on scalability, performance, reliability, and maintainability.
• Design and optimize data models, SQL queries, stored procedures, and data processing workflows.
• Workflow Orchestration & Data Quality
• Design and maintain Apache Airflow workflows, including scheduling, dependencies, retries, monitoring, and error handling.
• Develop automated data validation and quality checks using DBT and SQL.
• Implement appropriate monitoring, alerting, and observability for critical data pipelines.
• Troubleshoot and resolve complex data processing and quality issues.
• Cloud & Platform Engineering
• Develop data solutions using AWS and cloud‑based data warehouse technologies such as Snowflake.
• Work with cloud services for data storage, processing, compute, messaging, and serverless workloads.
• Support data platforms across development, QA, staging, and production environments.
• Optimize data workloads for performance, scalability, reliability, and cost.
• CI/CD & Production Support
• Develop and maintain CI/CD pipelines for data engineering applications and infrastructure.
• Apply Infrastructure‑as‑Code practices using Terraform or equivalent technologies.
• Support production deployments, incident resolution, root‑cause analysis, and continuous improvement.
• Maintain technical documentation, operational runbooks, and troubleshooting guides.
• Technical Leadership
• Take ownership of complex engineering initiatives from design through production.
• Conduct code reviews and promote engineering best practices.
• Mentor junior and mid‑level engineers and provide technical guidance.
• Collaborate with architecture, DevOps, product, and other technology teams.

Qualifications

• Programming: Python, SQL
• Orchestration: Apache Airflow or equivalent
• Data Transformation: DBT, SQL, PySpark
• Data Warehouse: Snowflake or equivalent cloud data warehouse
• Cloud: AWS or equivalent cloud platform
• AWS Services: S3, EMR, Lambda, SNS/SQS or equivalent
• CI/CD: Jenkins, GitHub, GitHub Actions, or equivalent

Mandatory Qualifications

• 5+ years of professional experience in data engineering or a related software engineering role.
• Strong hands‑on experience building and supporting production‑grade data pipelines.
• Strong knowledge of Apache Airflow, including DAG development, scheduling, dependencies, retries, and monitoring.
• Strong experience with Snowflake or equivalent cloud data warehouse technologies.
• Proven experience with DBT.
• Solid experience with AWS or another major cloud platform.
• Experience with CI/CD and Infrastructure‑as‑Code practices.
• Strong programming skills in Python and SQL.
• Demonstrated experience troubleshooting complex production issues and performing root‑cause analysis.
• Ability to independently own complex technical initiatives from design through implementation and production.
• Strong communication, problem‑solving, and technical documentation skills.

Preferred Qualifications

• Experience with large‑scale data processing.
• Experience with PySpark and EMR.
• Experience mentoring engineers and contributing to technical standards and architecture.

Benefits

• Finance: Competitive salary package, value‑based recognition awards, referral bonus;
• Career Development: Career coaching, global career opportunities, non‑linear career paths, internal development programmes for management and technical leadership;
• Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass‑it‑on sessions, workshops, conferences;
• Work‑Life Balance: Hybrid work and flexible working hours, employee assistance programme;
• Health: Global internal wellbeing programme, access to wellbeing apps;
• Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations.

Company Values

At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.