Data Analyst Expert
At a glance
Mid-level Data Analysis & BI role at GFT Technologies. Heredia.
Pay not stated Data Analysis & BI salary
dataroles summary, based on the employer's posting.
What you'll do
- Review current ETL checks and find places to automate validation
- Build pipeline checks that flag unusual data and compare incoming records with ETL results
- Automate completeness, consistency and record-count checks, then explain problems in AI-assisted reports
- Connect validation features to AWS data services and work with data, QA and business teams on rules and thresholds
- Shape data-quality measures and recommendations, and plan how validation can extend to more workloads
What you bring
- At least five years in data engineering, ETL development or data quality work
- A computer science, data engineering or software engineering degree, or equivalent experience
- Strong Python and advanced SQL skills, plus hands-on work with Amazon Bedrock and generative AI solutions
- Hands-on experience with AWS Lambda, Glue, Redshift, Athena and S3
Who this fits
This mid-level role suits someone who can combine ETL and data-quality work with anomaly detection and generative AI. You’ll collaborate with data engineering, QA and business teams to establish validation rules and thresholds.
From the employer
Job description_
About the Role_
Join our client and help bring AI-driven quality control to the data pipelines that power their business. You’ll analyze how ETL processes are validated today and design an AI-powered validation platform on AWS that detects anomalies, reconciles source and target data, and explains issues before they reach downstream consumers. This role blends data engineering, machine learning / anomaly detection, and generative AI (Amazon Bedrock), giving you the opportunity to shape both the data quality and governance strategy and the roadmap for AI-driven validation across additional workloads.
A day in this role_
- Analyze existing ETL validation processes and identify automation opportunities.
- Develop AI-driven validation frameworks for data pipelines.
- Implement anomaly detection for continuous data quality monitoring.
- Compare source data against ETL outputs to identify and track discrepancies.
- Automate record count, completeness, and consistency checks across pipelines.
- Generate AI-assisted validation reports that explain detected issues in clear, actionable terms.
- Integrate AI validation capabilities into AWS data platforms and workflows (Glue, Lambda, Redshift, Athena, S3).
- Evaluate how AI can be used to deliver in-depth QA analysis of deliverables.
- Collaborate with data engineering, QA, and business teams to define data quality rules, thresholds, and SLAs.
- AI-powered data validation platform.
- Automated ETL quality control framework.
- Anomaly detection models and monitoring dashboards.
- Validation reports and root-cause analysis capabilities.
- Data quality KPIs and governance recommendations.
- Roadmap for expanding AI-driven validation across additional workloads.
The expertise requested_
- Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, or related field (or equivalent experience).
- 5+ years of experience in data engineering, ETL development, or data quality roles.
- Strong programming skills in Python and advanced SQL.
- Hands-on experience with Amazon Bedrock and building generative AI solutions (prompt design, LLM-based analysis and reporting).
- Hands-on experience with AWS Lambda, AWS Glue, Amazon Redshift, Amazon Athena, and Amazon S3.
- Practical experience with machine learning and anomaly detection techniques applied to data (statistical, time-series, or ML-based).
- Solid understanding of data quality and data governance principles (completeness, accuracy, consistency, lineage).
- Experience designing source-to-target reconciliation and ETL testing strategies.
- Solid understanding of Agile, SDLC, and modern engineering best practices.
Desired or nice to have expertise_
- Experience with data quality frameworks such as AWS Glue Data Quality, Great Expectations, or Deequ.
- Experience building monitoring dashboards (Amazon QuickSight, CloudWatch, or similar).
- Familiarity with Amazon SageMaker or other ML platforms for model training and deployment.
- Exposure to agentic AI workflows and AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Cursor).
- AWS certifications (e.g., Data Engineer – Associate, Machine Learning – Specialty)
Our Core values are focus to inclusion and diversity, all qualified applicants will be considered for employment and will go thru a fair recruitment process regardless of their race, religion, gender identity, sexual orientation, national origin or disability status.
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