Data Engineer
fracto solutionsSan Antonio (TX)
About the role
Key Responsibilities:
Build and maintain data pipelines (ETL/ELT) to ingest data from multiple sources Develop and optimize SQL queries for analytics and reporting use cases Assist in managing and scaling data warehouses and data lakes Perform data validation, cleansing, and quality monitoring Collaborate with Data Analysts, Data Scientists, and Product teams Support cloud-based data solutions on AWS, GCP, or Azure Document data models, pipelines, and technical workflows Troubleshoot and resolve pipeline failures or data discrepancies Follow best practices related to data security, governance, and compliance
Required Qualifications:
Master’s degree (required) in Data Science, Computer Science, Information Systems, Engineering, Analytics, or a related field Strong proficiency in SQL (joins, window functions, aggregations)Working knowledge of Python for data processing and automation Understanding of data modeling, ETL concepts, and database design Familiarity with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle Exposure to at least one cloud platform (AWS, GCP, or Azure)Knowledge of Git or other version control systems Strong analytical thinking and problem-solving skills Excellent written and verbal communication skills
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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