Data Engineer

CEI; Richmond (VA); Hybrid; USD 100,000 - 120,000; Full time

Job summary

CEI in Richmond, VA is seeking a Senior Data Engineer to design, build, and optimize enterprise data pipelines. You will own technical direction, mentor teams, and drive best practices across data engineering and platform teams.

The role requires deep hands-on expertise with SQL, ELT, and modern data warehouses, plus experience in data pipelines, streaming, and observability. Hybrid work arrangement is available in a data-driven environment.

Qualifications

• Advanced SQL, ELT patterns, and performance tuning.
• Experience with Oracle Exadata, Snowflake, or similar warehouses.
• Hands-on with ETL/ELT tools (Talend, dbt, Informatica).
• Data warehousing architecture and dimensional modeling.
• Designing and supporting large-scale production data pipelines.
• Scripting in Python and shell.
• Data virtualization tools (Denodo, Composite, Dremio, Starburst).
• DataOps, CI/CD, and observability practices.
• 5–7+ years of Data Engineering experience.
• ETL development and possible weekend/on-call work.

Responsibilities

• Build end-to-end data pipelines for analytics and ML use cases.
• Develop scalable batch and incremental processing workflows.
• Review data modeling, transformations, and performance strategies.
• Evaluate and integrate tooling and platform capabilities.
• Build complex, high-volume data pipelines with SQL-centric ETL/ELT patterns.
• Design scalable streaming pipelines for real-time data.
• Lead performance tuning across pipelines, warehouses, and workloads.
• Ensure pipelines are resilient, observable, and production-ready.
• Implement enterprise-grade error handling, restartability, and monitoring.
• Maintain scalable, low-latency streaming pipelines using Kafka, Kinesis, or Spark Streaming.

Skills

SQL

ELT patterns

performance tuning

data pipelines

Python

shell

DataOps

CI/CD

observability

5–7+ years

Education

Bachelor's Degree or higher

Computer Science/Info Systems/Math

Tools

Talend

dbt

Informatica

Oracle Exadata

Snowflake

Denodo

Composite

Dremio

Starburst

Apache Kafka

Kafka Connect

Spark Streaming

Apache Flink

Cribl

Kinesis

Job description

Richmond , Virginia Contract Hybrid Aug 3, 2026

Role Summary

The Senior Data Engineer is a hands-on expert and technical leader, actively engaged in designing, building, and optimizing scalable, reliable data pipelines at an enterprise level. This role not only guides architectural decisions but also directly implements advanced ELT solutions, troubleshoots complex data challenges, and ensures best practices through practical, high-impact contributions.

This role combines deep hands-on expertise with technical ownership, mentoring, and architectural alignment. The Senior Data Engineer drives and implements data engineering best practices, ensures high standards for quality and security, and partners with architecture and platform teams to improve the overall data ecosystem.

Required Skills & Experience

• Advanced expertise in SQL, ELT patterns, and performance tuning.
• Strong experience with Oracle Exadata, Snowflake or similar cloud/on-prem data warehouses.
• Hands-on experience with enterprise ETL/ELT platforms (e.g., Talend, dbt, Informatica).
• Deep understanding of data warehousing architecture and dimensional modeling.
• Experience designing and supporting large-scale, production data pipelines.
• Strong scripting experience (Python, shell).
• Experience with data virtualization tools (e.g., Denodo, Composite, Dremio, Starburst).
• Experience with DataOps practices, CI/CD, and observability.
• Required 5 to 7+ years of Data Engineering experience.
• ETL development and process support; may require weekend/off-business-hours work.

Key Responsibilities

• Build end-to-end data pipelines and ETL/ELT solutions to support analytics, reporting, and AI/ML use cases.
• Apply scalable patterns for batch and incremental processing by developing, testing, and deploying data workflows.
• Review and implement data modeling, transformation logic, and performance strategies.
• Evaluate, select, and integrate tooling, frameworks, and platform capabilities.
• Build complex, high-volume data pipelines using SQL-centric ETL/ELT patterns.
• Design and implement scalable streaming pipelines to process real-time data.
• Lead performance tuning efforts across pipelines, warehouses, and workloads.
• Ensure data pipelines are resilient, observable, and production ready.
• Implement enterprise-grade error handling, restartability, and monitoring.
• Build and maintain scalable, low-latency streaming data pipelines using Kafka, Kinesis, or Spark Streaming.
• Perform on-the-fly data cleaning, validation, and enrichment.
• Utilize indexing and partitioning strategies to optimize warehouse and big data environments.
• Implement standards for data quality checks, validation, and reconciliation.
• Ensure pipelines meet security, access control, and governance requirements.
• Partner with governance and DataOps teams on metadata, lineage, and auditability.
• Improve operational monitoring, alerting, and incident response processes.
• Identify reliability, performance, and cost optimization opportunities.
• Support production troubleshooting and root cause analysis.
• Investigate data quality incidents and identify design/coding gaps.
• Participate in design and code reviews.
• Partner with infrastructure teams, application teams, and architects on complex transformations.
• Translate ambiguous requirements into technical solutions.
• Work across complex multi-platform environments.
• Delivering at Pace
• Effective Communication
• Ownership and Adaptability
• Ability to Work Independently
• Achievement Orientation
• Concern for Quality
• Flexibility

Preferred Qualifications

• Experience supporting AI/ML or advanced analytics pipelines.
• Cloud platform experience (AWS, Azure, or GCP).
• Prior experience influencing enterprise data standards or reference architecture.
• Experience optimizing cost and performance in cloud data warehouses.
• Hands-on experience with Cribl, Apache Kafka, Kafka Connect, Spark Streaming, or Apache Flink.

Education

• Bachelor's Degree or higher required
• Computer Science, Information Systems, Mathematics, or related discipline

Preferred Industry Background

• High preference for candidates with large-scale utility industry experience.
• Will also consider candidates supporting large-scale capital projects.

Data Engineer

Richmond, VA 23219 (hybrid)
Pay: $100,000-120,000

Role Summary

The Senior Data Engineer is a hands-on expert and technical leader, actively engaged in designing, building, and optimizing scalable, reliable data pipelines at an enterprise level. This role not only guides architectural decisions but also directly implements advanced ELT solutions, troubleshoots complex data challenges, and ensures best practices through practical, high-impact contributions.

This role combines deep hands-on expertise with technical ownership, mentoring, and architectural alignment. The Senior Data Engineer drives and implements data engineering best practices, ensures high standards for quality and security, and partners with architecture and platform teams to improve the overall data ecosystem.

Required Skills & Experience

• Advanced expertise in SQL, ELT patterns, and performance tuning.
• Strong experience with Oracle Exadata, Snowflake or similar cloud/on-prem data warehouses.
• Hands-on experience with enterprise ETL/ELT platforms (e.g., Talend, dbt, Informatica).
• Deep understanding of data warehousing architecture and dimensional modeling.
• Experience designing and supporting large-scale, production data pipelines.
• Strong scripting experience (Python, shell).
• Experience with data virtualization tools (e.g., Denodo, Composite, Dremio, Starburst).
• Experience with DataOps practices, CI/CD, and observability.
• Required 5 to 7+ years of Data Engineering experience.
• ETL development and process support; may require weekend/off-business-hours work.

Key Responsibilities

• Build end-to-end data pipelines and ETL/ELT solutions to support analytics, reporting, and AI/ML use cases.
• Apply scalable patterns for batch and incremental processing by developing, testing, and deploying data workflows.
• Review and implement data modeling, transformation logic, and performance strategies.
• Evaluate, select, and integrate tooling, frameworks, and platform capabilities.
• Build complex, high-volume data pipelines using SQL-centric ETL/ELT patterns.
• Design and implement scalable streaming pipelines to process real-time data.
• Lead performance tuning efforts across pipelines, warehouses, and workloads.
• Ensure data pipelines are resilient, observable, and production ready.
• Implement enterprise-grade error handling, restartability, and monitoring.
• Build and maintain scalable, low-latency streaming data pipelines using Kafka, Kinesis, or Spark Streaming.
• Perform on-the-fly data cleaning, validation, and enrichment.
• Utilize indexing and partitioning strategies to optimize warehouse and big data environments.
• Implement standards for data quality checks, validation, and reconciliation.
• Ensure pipelines meet security, access control, and governance requirements.
• Partner with governance and DataOps teams on metadata, lineage, and auditability.
• Improve operational monitoring, alerting, and incident response processes.
• Identify reliability, performance, and cost optimization opportunities.
• Support production troubleshooting and root cause analysis.
• Investigate data quality incidents and identify design/coding gaps.
• Participate in design and code reviews.
• Partner with infrastructure teams, application teams, and architects on complex transformations.
• Translate ambiguous requirements into technical solutions.
• Work across complex multi-platform environments.

Behavioral Expectations

• Delivering at Pace
• Collaborative Teamwork
• Effective Communication
• Ownership and Adaptability
• Ability to Work Independently
• Achievement Orientation
• Self-Starter
• Concern for Quality
• Flexibility

Preferred Qualifications

• Experience supporting AI/ML or advanced analytics pipelines.
• Cloud platform experience (AWS, Azure, or GCP).
• Prior experience influencing enterprise data standards or reference architecture.
• Experience optimizing cost and performance in cloud data warehouses.
• Hands-on experience with Cribl, Apache Kafka, Kafka Connect, Spark Streaming, or Apache Flink.

Education

• Bachelor's Degree or higher required
• Computer Science, Information Systems, Mathematics, or related discipline

Preferred Industry Background

• High preference for candidates with large-scale utility industry experience.
• Will also consider candidates supporting large-scale capital projects.