Analytics Engineer, Mid-Level

Extractta

Analytics Engineer developing and maintaining analytical data pipelines using Python, Spark, and AWS at Extractta. Collaborating with engineering and analytics teams for data quality and integration.

Posted 7/16/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing data pipelines using Python and Spark, with a strong focus on AWS services and Infrastructure as Code practices. Proficient in SQL for data modeling and analytical queries, while ensuring data governance and observability.

Highest-signal resume keywords

Python Data ProcessingSpark Large-Scale ProcessingAWS Data ServicesInfrastructure as Code (IaC) with TerraformSQL for Data Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentSpark Job OptimizationSQLData TransformationData IngestionData DeliveryDistributed Query EnginesVersion Control (Git)Data GovernanceData Quality Best Practices

Tools & Technologies

AirflowAWS S3AWS EMRKubernetesTerraformTrino

Industry Keywords

Data InfrastructureData ProcessingObservabilityGovernanceScalability

Tech Stack

Tools & technologies

AirflowAWSKubernetesPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Work with Python and Spark for large-scale data processing, using Airflow as the orchestration tool.
Own extraction and integration pipelines on AWS, operating in distributed environments such as Kubernetes and EMR.
Work on data storage and analytical layers using SQL, S3 and Trino, supporting data modeling and analytical queries.
Participate in defining and maintaining data infrastructure with Terraform, backed by a proprietary Infrastructure-as-Code (IaC) framework.
Collaborate with engineering, analytics and business teams on Spark job tuning and optimization, pipeline versioning and standardization, and contribute to platform observability, governance and scalability best practices.

Requirements

What you’ll need

Solid experience developing data pipelines (ingestion, transformation, and delivery).
Proficient in Python for data processing.
Experience with Spark for large-scale processing.
Familiarity with Airflow for workflow orchestration.
Experience with AWS data services, especially S3 and EMR.
Experience with distributed environments and containerization using Kubernetes.
Strong SQL skills for data modeling and analytical queries.
Experience with distributed query engines such as Trino.
Knowledge of Infrastructure as Code (IaC) using Terraform.
Experience with version control (Git).
Experience optimizing and tuning Spark jobs.
Knowledge of data governance, observability and data quality best practices.

Benefits

Comp & perks

Competitive compensation based on experience
Opportunities for growth within the company and participation in strategic projects
Dynamic and challenging work environment
Opportunity to work at a company experiencing strong market growth.