FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and operating large-scale data pipelines using Spark, Java, and Python, while ensuring data quality, observability, and governance. Proficient in integrating AI capabilities and collaborating with data scientists and ML engineers on MLOps infrastructure.
Highest-signal resume keywords
Java ProficiencyPython ProficiencySpark ExpertiseData Engineering ExperienceMLOps Practices
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DesignETL/ELT Pipeline DesignData Quality and ObservabilityDistributed SystemsEvent-Driven PatternsSystem DesignDomain-Oriented DesignLow-Level DesignTesting StrategyCode Review Standards
Soft Skills
Ownership MindsetTechnical JudgmentAutonomy
Tools & Technologies
Hadoop/HDFSHiveKafkaAirflowDelta LakeBigQueryDatabricksKubernetesCI/CDCloud-Native Practices
Certifications & Qualifications
Bachelor's Degree in Computer ScienceMaster's Degree in Computer Science
Industry Keywords
Big DataData GovernanceData TransformationAI/GenAIAgentic WorkflowsMicroservicesAPI DesignReliability MetricsContinuous ImprovementBlameless Post-Incident Reviews
Tech Stack
Tools & technologiesAirflowBigQueryCloudDistributed SystemsETLHadoopHDFSJavaKafkaKubernetesMicroservicesPythonSpark
About the role
Key responsibilities & impact- Design, build, and operate large-scale batch and streaming data pipelines using Spark, Java, and Python
- Apply software engineering fundamentals, including clean architecture, automated testing, CI/CD, and code review discipline
- Apply domain-oriented design, system design, and low-level design rigor to data and platform problems
- Build and maintain services, APIs, and distributed systems supporting data pipelines
- Integrate AI, GenAI, and agentic capabilities into the data platform
- Partner with data scientists and ML engineers on feature pipelines, model-serving data paths, and MLOps infrastructure
- Own data quality, observability, and governance for pipelines and datasets
- Participate in and lead architecture and design reviews
- Set technical direction and best practices; mentor engineers and influence data engineering standards
- Evaluate and adopt emerging Big Data, cloud, and AI/agentic technologies pragmatically
Requirements
What you’ll need- 8+ years of professional software engineering experience with significant hands-on data engineering at scale
- Strong proficiency in Java and Python
- Strong working knowledge of Spark for distributed data processing
- Operational Excellence and Engineering Excellence skills, including SLO/SLA ownership, incident management, blameless post-incident reviews, reliability and quality metrics, and continuous improvement
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
- Deep experience with Spark, Hadoop/HDFS, Hive, Kafka, workflow orchestration such as Airflow, batch and streaming processing, and large-scale data transformation
- Experience with data modeling, ETL/ELT pipeline design, data quality and observability, data governance, and lakehouse architectures such as Delta Lake, BigQuery, and Databricks
- Hands-on experience integrating model APIs and GenAI/LLM tooling, building or operating agentic workflows, embeddings/vector search, and MLOps practices
- Working literacy in microservices, API design, Kubernetes or equivalent orchestration, CI/CD, and cloud-native practices
- Strong distributed systems, event-driven patterns, service contracts, asynchronous processing, fault tolerance, system design, domain-oriented design, and low-level design skills
- Strong software engineering craftsmanship, including testing strategy, code review standards, and design patterns
- Ability to translate ambiguous data/business problems into clear technical execution plans
- Strong ownership mindset and technical judgment; comfortable operating with high autonomy
- Minimum qualification alternatives: Bachelor's degree in Computer Science and 4 years' experience in software engineering or related field; or 6 years' experience in software engineering or related field; or Master's degree in Computer Science and 2 years' experience in software engineering or related field
- 3 years' experience in data engineering, database engineering, business intelligence, or business analytics
Benefits
Comp & perks- Incentive awards for performance
- Maternity and parental leave
- PTO
- Health benefits
- Flexibility for associates to manage their personal lives