Data Engineer

Blackcanyonconsulting

Bethesda, MD

On-siteCareer-pivot friendly

Who this role is best for

Aimed at mid-to-senior-level engineers who build scalable data infrastructure for AI/ML workloads.

Best fit for

Candidates with 5+ years of ETL/ELT pipeline experience and a track record of optimizing data workflows
— “Experience building and optimizing ETL and/or ELT workflows”
Individuals who have worked with databases, data lakes, and warehouses, and can integrate them into unified platforms
— “Experience integrating databases, data lakes, data warehouses, or similar data platforms”
Professionals who can design data infrastructure for high-volume systems and ensure operational readiness
— “Experience designing data infrastructure for high-volume or distributed systems”

Things to consider

The role demands a strong focus on data quality and reliability across the full lifecycle
— “Ensure data reliability, accessibility, quality, and operational readiness”
Candidates must be prepared to work with technical stakeholders and support AI/ML environments
— “Collaborate with data scientists, software engineers, analysts, and other technical stakeholders”

How to stand out

Highlight experience with feature engineering and cloud platforms in your resume and interviews
— “Experience developing feature engineering workflows or working with feature stores”
Emphasize your ability to document and maintain complex data processes
— “Document data pipelines, integrations, transformations, and technical processes”
Showcase your skills in monitoring and troubleshooting data pipelines for performance
— “Experience monitoring, troubleshooting, and optimizing data pipelines”
Demonstrate your ability to configure and integrate modern data architectures
— “Configure and integrate databases, data lakes, data warehouses, feature stores, and other data platforms”
Demonstrate a track record of ensuring data quality through validation and testing
— “Implement processes to validate data accuracy, completeness, and consistency”

Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Designed and maintained scalable data pipelines
Developed and maintained ETL/ELT processes

Typical background

Experience designing and maintaining data pipelinesBackground in data engineering

Skills & requirements

Required

Data Pipeline DesignEtl/elt WorkflowsDatabase IntegrationData TransformationAPI DevelopmentData Reliability And QualityMonitoring And Troubleshooting

Preferred

Machine Learning EnvironmentsFeature EngineeringCloud-based Data PlatformsData Validation And Testing

Stack & domain

Data PipelinesEtl/eltRelational DatabasesNon-relational DatabasesData LakesData WarehousesFeature StoresApisService LayersData TransformationData ModelingData ArchitectureData ReliabilityData AccessibilityData QualityData Operational ReadinessMonitoringTroubleshootingOptimizing Data PipelinesAi/ml EnvironmentsFeature EngineeringHigh-volume SystemsDistributed SystemsCloud-based Data PlatformsAutomated Data Pipeline MonitoringAlertingData ValidationData TestingData Quality ControlsCollaborationProblem-solvingProgrammingCommunicationTeamworkDocumentationData EngineeringAi/ml

About the role

Original posting from Blackcanyonconsulting via Greenhouse

Overview
Black Canyon Consulting (BCC) is searching for a Data Engineer to support the design, development, and maintenance of data infrastructure that enables artificial intelligence, machine learning, and analytical workloads. The Data Engineer will help ensure that data is reliable, accessible, scalable, and operationally ready throughout the full lifecycle from ingestion through deployment.
Job Description
The Data Engineer will design, implement, maintain, and optimize data pipelines, APIs, data transformation workflows, and supporting infrastructure. This position will work with databases, data lakes, data warehouses, feature stores, and other data platforms to ensure information can be efficiently ingested, transformed, accessed, and used by analytical and AI-enabled systems.
The Data Engineer will also develop and maintain ETL/ELT processes, feature engineering workflows, and service layers that expose data or model endpoints to downstream systems and applications.
Required Skills
Experience designing, developing, and maintaining data pipelines
Experience building and optimizing ETL and/or ELT workflows
Experience working with relational and/or non-relational databases
Experience integrating databases, data lakes, data warehouses, or similar data platforms
Experience developing data transformation workflows
Experience supporting data used for analytical, AI, or machine learning workloads
Experience developing or integrating APIs and service layers
Strong understanding of data reliability, accessibility, quality, and operational readiness
Experience monitoring, troubleshooting, and optimizing data pipelines
Understanding of data modeling and data architecture principles
Strong programming and problem-solving skills
Ability to work collaboratively with software engineers, data scientists, analysts, and other technical stakeholders
Experience Requirements by Level:
Senior: 8+ years of relevant experience
Mid-Level: 5+ years of relevant experience
Junior: 3+ years of relevant experience
Desired Skills
Experience supporting machine learning or artificial intelligence environments
Experience developing feature engineering workflows or working with feature stores
Experience designing data infrastructure for high-volume or distributed systems
Experience with cloud-based data platforms and infrastructure
Experience developing APIs that expose data, analytical results, or model endpoints
Experience with automated data pipeline monitoring and alerting
Experience optimizing data pipelines for performance, reliability, and scalability
Experience working with modern data lakehouse or warehouse architectures
Experience implementing data validation, testing, and quality controls
Job Responsibilities
Design, implement, and maintain scalable data pipelines supporting analytical and AI/ML workloads
Build, monitor, troubleshoot, and optimize ETL and ELT data pipelines
Develop workflows for ingesting, transforming, processing, and delivering data
Configure and integrate databases, data lakes, data warehouses, feature stores, and other data platforms
Develop and maintain data transformation and feature engineering processes
Ensure data reliability, accessibility, quality, and operational readiness
Develop APIs and service layers to expose data or model endpoints to downstream applications and systems
Collaborate with data scientists, software engineers, analysts, and other technical teams to support data requirements
Monitor pipeline performance and resolve data processing or integration issues
Implement processes to validate data accuracy, completeness, and consistency
Support deployment and operation of data infrastructure throughout the system lifecycle
Identify opportunities to improve pipeline scalability, performance, maintainability, and efficiency
Document data pipelines, integrations, transformations, and technical processes
Benefits and Salary
We attract the best people in the business with our competitive benefits package that includes medical, dental and vision coverage, 401k plan with employer contribution, paid holidays, vacation, and tuition reimbursement.
If you enjoy being a part of a high performing, professional service and technology focused organization, please apply today!

Source: Blackcanyonconsulting careers (Greenhouse)