Analytics Engineer

Doist

Houston, Northern (TX, KY)

On-site

USD 110,000 - 140,000

Full time

4 days ago

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Benefits offered by this job

Bonus opportunity

401K

Full-Time salary

Job summary

Doist is seeking an Analytics Engineer in Houston, TX, to design, build, and maintain our enterprise data platform. You will work across ETL/ELT pipelines, data warehouses, and semantic models, ensuring data quality and reliable analytics for business users.

The role emphasizes SQL proficiency, data modeling, and hands-on data engineering within a hybrid environment (onsite 4 days per week).

Qualifications

Bachelor’s degree or equivalent professional experience in a related field
3+ years of experience in analytics engineering, data engineering, BI, data warehousing, or related discipline
Strong hands-on experience with SQL and relational databases
Experience designing and developing data warehouse solutions
Experience developing and supporting ETL/ELT pipelines
Experience with Azure Data Factory or similar cloud-based data integration platform
Strong understanding of data modeling, including dimensional modeling and analytical data structures
Experience developing semantic models and curated/certified datasets
Experience troubleshooting data pipelines, identifying root causes, and resolving data issues
Understanding of data quality, validation, monitoring, and automated testing concepts
Strong analytical and problem solving skills
Ability to document technical solutions clearly and effectively

Responsibilities

Design, develop, and maintain enterprise data warehouse structures, tables, views, and related data assets
Develop efficient and scalable SQL queries, stored procedures, transformations, and data models
Build and maintain ETL/ELT processes that integrate data from multiple enterprise sources
Develop and support data pipelines using Azure Data Factory
Monitor data pipelines and proactively identify, troubleshoot, and resolve failures and data processing issues
Implement appropriate error handling, logging, alerting, and recovery processes
Support the ongoing enhancement and evolution of the enterprise data platform
Design and maintain semantic models that enable consistent, trusted access to enterprise data
Develop and maintain certified datasets for reporting, analytics, and BI
Translate business and analytical requirements into scalable data models and technical solutions
Establish consistent definitions, relationships, calculations, and business logic across analytical datasets
Partner with data analysts, BI developers, and business stakeholders to ensure data solutions meet reporting needs
Develop and automate data quality checks, validation processes, and monitoring capabilities
Identify data quality issues and work with appropriate teams to determine root causes and solutions
Optimize SQL queries, data models, and pipelines for performance and scalability
Perform platform performance tuning and improve reliability and efficiency
Monitor overall data platform health and contribute to capacity improvements
Create and maintain technical documentation for data models, pipelines, transformations, definitions
Establish and follow standards for SQL development, data modeling, pipeline development, and data quality
Contribute to data engineering best practices, development standards, and platform governance
Participate in code reviews to improve quality and maintainability

Skills

SQL proficiency

Data modeling

ETL/ELT pipelines

Azure Data Factory

Analytical thinking

Documentation

Education

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering or related field

Tools

Azure Data Factory

Power BI

Git

CI/CD

Azure SQL / SQL Server

Job description

ANALYTICS ENGINEER

Location: Houston, TX

Employment Type: Full-Time

Work Model: Hybrid - Onsite 4 days per week

Reports To: Sr. Manager, Data & Analytics

ABOUT THE ROLE

We are seeking an Analytics Engineer to join our Data & Analytics team and play a key role in designing, developing, and maintaining our enterprise data platform. This position combines strong SQL development, data engineering, data warehousing, semantic modeling, and analytics expertise to ensure reliable, scalable, and high-quality data is available to business users and analytics teams. The Analytics Engineer will support the Sr. Manager, Data & Analytics by executing the hands-on technical work required to maintain, optimize, and continuously evolve the enterprise data platform. The ideal candidate is comfortable working across the data stack—from ETL/ELT pipelines and data warehouses to semantic models and certified datasets—and has a strong focus on data quality, performance, reliability, and maintainability.

KEY RESPONSIBILITIES

Data Engineering & Data Platform Development

Design, develop, and maintain enterprise data warehouse structures, tables, views, and related data assets
Develop efficient and scalable SQL queries, stored procedures, transformations, and data models
Build and maintain ETL/ELT processes that integrate data from multiple enterprise sources
Develop and support data pipelines using Azure Data Factory
Monitor data pipelines and proactively identify, troubleshoot, and resolve failures and data processing issues
Implement appropriate error handling, logging, alerting, and recovery processes
Support the ongoing enhancement and evolution of the enterprise data platform

Analytics & Semantic Modeling

Design and maintain semantic models that enable consistent, trusted, and user-friendly access to enterprise data
Develop and maintain certified datasets for reporting, analytics, and business intelligence
Translate business and analytical requirements into scalable data models and technical solutions
Establish consistent definitions, relationships, calculations, and business logic across analytical datasets
Partner with data analysts, BI developers, and business stakeholders to ensure data solutions meet reporting and analytical needs

Data Quality & Performance

Develop and automate data quality checks, validation processes, and monitoring capabilities
Identify data quality issues and work with appropriate teams to determine root causes and implement solutions
Optimize SQL queries, data models, and pipelines to improve performance and scalability
Perform platform performance tuning and identify opportunities to improve reliability and efficiency
Monitor overall data platform health and contribute to performance and capacity improvements

Documentation & Best Practices

Create and maintain technical documentation for data models, pipelines, transformations, processes, and data definitions
Establish and follow standards for SQL development, data modeling, pipeline development, and data quality
Contribute to data engineering best practices, development standards, and platform governance
Participate in code reviews and provide recommendations to improve the quality, maintainability, and performance of data solutions

REQUIRED QUALIFICATIONS

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field, or equivalent professional experience
3+ years of experience in analytics engineering, data engineering, business intelligence, data warehousing, or a related discipline
Strong hands‑on experience with SQL and relational databases
Experience designing and developing data warehouse solutions
Experience developing and supporting ETL/ELT pipelines
Experience with Azure Data Factory or a similar cloud‑based data integration platform
Strong understanding of data modeling, including dimensional modeling and analytical data structures
Experience developing semantic models and curated/certified datasets
Experience troubleshooting data pipelines, identifying root causes, and resolving data issues
Understanding of data quality, validation, monitoring, and automated testing concepts
Strong analytical and problem solving skills
Ability to document technical solutions clearly and effectively

PREFERRED QUALIFICATIONS

Experience working within the Microsoft Azure data and analytics ecosystem
Experience with Power BI or similar business intelligence platforms
Experience with Azure SQL, SQL Server, Azure Synapse Analytics, Microsoft Fabric, or similar data platforms
Experience with source control and collaborative development practices such as Git
Experience with CI/CD and deployment processes for data solutions
Knowledge of data governance, metadata management, and enterprise data standards
Experience optimizing large-scale SQL queries and data warehouse workloads
Familiarity with automated data quality and pipeline monitoring frameworks
Experience working in an enterprise environment with multiple data sources and business domains

Benefits