Data Engineer

Stack Exchange

. Embed with engineering teams to turn application requirements into data models, pipeline designs, and clear interfaces between systems .

Posted 10/16/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and operating production data pipelines using SQL, Python, and cloud technologies, while ensuring data integrity and observability. Proven ability to collaborate with engineering teams and mentor peers in a distributed environment.

Highest-signal resume keywords

SQLPythonData Pipeline ArchitectureCloud Data TechnologiesAPI Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingQuery OptimizationIncremental ProcessingData Quality ChecksAutomated TestingVersion ControlCI/CD ToolingObservability LoggingTransformation WorkflowsData Lifecycle Management

Soft Skills

CommunicationCollaborationMentoringSelf-DirectionProblem-Solving

Tools & Technologies

DatabricksApache SparkDelta LakeAzureGCP

Industry Keywords

Production Data OperationsData FreshnessData CompletenessTenant BoundariesRegional Constraints

Tech Stack

Tools & technologies

ApacheAzureCloudGoogle Cloud PlatformPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Embed with engineering teams to turn application requirements into data models, pipeline designs, and clear interfaces between systems
Design, build, and operate production data operations using SQL, Python, PySpark, and cloud data technologies such as Databricks
Integrate APIs, databases, and structured, semi-structured, and unstructured content
Build ingestion and transformation workflows for application services and downstream capabilities
Design customer-aware data flows preserving tenant boundaries, source permissions, data lifecycle requirements, updates, deletions, and regional constraints
Make pipelines resilient through incremental processing, checkpointing, safe retries, backfills, replay, and data-quality checks
Build observability for data freshness, completeness, processing progress, and pipeline failures
Evaluate architecture and tooling for reliability, performance, maintainability, and operating cost
Maintain and improve shared datasets and pipelines supporting application features and internal analytics
Contribute to design reviews, testing, documentation, and deployment practices
Share knowledge and mentor less experienced engineers

Requirements

What you’ll need

5+ years of relevant engineering experience, including building and operating production data pipelines and contributing to their architecture
Strong SQL and Python skills
Experience with relational databases, data modeling, and query optimization
Experience integrating external APIs and working with changing schemas, imperfect source data, and incremental updates
Experience applying software engineering practices to data systems, including version control, automated testing, code review, CI/CD tooling, observability logging and monitoring
Ability to investigate issues across ingestion, transformation, and downstream delivery
Ability to explain technical tradeoffs and collaborate with technical and non-technical stakeholders
Track record of sharing knowledge and helping other engineers grow
Communication and self-direction needed to work effectively on a distributed team
Experience with Azure/GCP and cloud data platforms such as Databricks, Apache Spark, Delta Lake, or comparable technologies

Benefits

Comp & perks

Competitive Base Salary
Generous paid vacation
Generous parental leave, family care leave, and unlimited sick days
Industry-leading health benefits applicable per country of residence for all full-time employees
Company-paid Life Insurance
Home Internet stipend
Professional allocation for growth and development
One-time allowance to assist with home office setup
Company-paid access to Calm, Prosus Academy and Overdrive