Data Engineer

Troveo AI

. Design, build, and maintain batch and streaming ELT/ETL data pipelines .

Posted 10/27/2026full-timeRemote • United StatesMid-LevelSenior💰 $160,000 - $175,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining ELT/ETL data pipelines, with strong proficiency in SQL and Python. Capable of implementing data modeling best practices and ensuring data quality and governance across various cloud data platforms.

Highest-signal resume keywords

SQL ProficiencyPython ProgrammingData Pipeline DevelopmentCloud Data PlatformsData Modeling Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonData Pipeline DevelopmentData ModelingData TransformationData Quality FrameworksData AnalysisData GovernanceCI/CD PracticesTroubleshooting

Soft Skills

Strong CommunicationCollaboration

Tools & Technologies

DbtApache AirflowSnowflakeBigQueryRedshiftDatabricksLookerTableauPower BIKafka

Industry Keywords

ELTETLData PipelinesData QualityData ObservabilityStreaming Data TechnologiesDimensional ModelingData WarehousingBusiness IntelligenceData Governance

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheBigQueryCloudETLKafkaPythonSnowflakeSQLTableau

About the role

Key responsibilities & impact

Design, build, and maintain batch and streaming ELT/ETL data pipelines
Optimize pipelines for performance, reliability, scalability, and cost
Design and implement conceptual, logical, and physical data models, including dimensional, star, and snowflake schemas
Build and maintain transformation layers using modern tools such as dbt
Apply data modeling best practices, versioning, testing, and documentation
Write SQL queries for data exploration, ad-hoc analysis, and troubleshooting
Support reports, dashboards, and self-service analytics assets
Translate business questions into data requirements and deliver actionable insights or datasets
Monitor data pipelines and delivery processes to meet timeliness, freshness, and accuracy SLAs
Identify, troubleshoot, and resolve data issues affecting downstream consumers and business operations
Manage data availability and quality incidents and participate in on-call rotations as needed
Implement data quality checks, observability, and alerting
Automate operational tasks and improve data delivery processes
Collaborate with analysts, data scientists, engineers, and business stakeholders
Document data pipelines, models, lineage, and processes
Contribute to data governance, security, and data platform best practices

Requirements

What you’ll need

5+ years of professional experience in data engineering or a closely related role
Strong proficiency in SQL and Python
Hands-on experience building and maintaining data pipelines
Experience with cloud data platforms/warehouses such as Snowflake, BigQuery, Redshift, or Databricks
Experience with data orchestration tools such as Apache Airflow, Dagster, Prefect, or similar
Solid understanding of data modeling techniques and dimensional modeling
Experience performing data analysis and working with BI/visualization tools such as Looker, Tableau, or Power BI
Proven ability to troubleshoot data issues and support operational reliability/SLAs
Strong communication skills and ability to collaborate with technical and non-technical stakeholders
Experience with dbt for data transformation and modeling
Knowledge of data observability/monitoring tools
Experience with real-time/streaming data technologies such as Kafka or Flink
Familiarity with CI/CD practices for data pipelines
Experience in data quality frameworks and governance
Bachelor’s degree in Computer Science, Engineering, or a related quantitative field, or equivalent practical experience
Ability to work from anywhere in the US
Legal authorization to work in the United States
Willingness to undergo a background check in accordance with local law/regulations

Benefits

Comp & perks

Medical, dental, and vision coverage (100% employer-paid for employees)
Unlimited PTO
Paid holidays
Remote-first work policy
Annual learning stipend
Access to top conferences
Direct mentorship from experienced founders
Competitive equity package
Budget for equipment and software
High-trust, low-ego environment focused on impact, transparency, and work-life balance
Occasional team offsites