Senior Data Analytics Engineer - Sql, Python, Ml (Remote)

Anyone Ai Inc.; Buenos Aires; Remote; ARS 123,632,000 - 169,994,000; Full time

Job summary

Endava is seeking a Data Engineer to design, build, and maintain scalable ETL/ELT pipelines that ingest, transform, validate, and deliver data across environments. You will craft modular, configurable data processing solutions and contribute to architectural decisions for scalable, reliable systems.

Responsibilities include building Airflow workflows, validating data with DBT/SQL, and ensuring observability and cost-efficient operations on AWS/Snowflake platforms.

Qualifications

• 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.

Responsibilities

• 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.
• 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.

Skills

ETL/ELT pipelines

SQL

Data modeling

Data architecture

Cloud platforms

Tools

Apache Airflow

DBT

Snowflake

AWS

Terraform

CI/CD tooling

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.
• 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.