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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and managing production data pipelines, with a strong focus on data modeling, quality assurance, and collaboration with cross-functional teams. Proficient in Python, SQL, and AWS data services, with a solid understanding of data standards and pipeline automation.
Highest-signal resume keywords
Production Data Pipeline DevelopmentPython ProgrammingAdvanced SQL QueryingData Modeling in PostgresAWS Data Services
ATS Keywords
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Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DesignData Quality ChecksData ModelingWorkflow OrchestrationSchema DesignIndexingPartitioningData ParsingFeature Pipeline PreparationEvent-Driven Data Processing
Soft Skills
Clear CommunicationPragmatic Decision-Making
Tools & Technologies
AWS S3Managed DatabasesStreaming PlatformsCI/CD for Data PipelinesPgvector
Industry Keywords
Data StandardsData ResidencySOC 2GDPRConstruction Scheduling ToolsEngineering DataEnergy Sector Data
Tech Stack
Tools & technologiesAWSPostgresPythonSQL
About the role
Key responsibilities & impact- Build robust pipelines ingesting project data from scheduling and planning tools
- Model data so every update is preserved and any two points in time can be compared
- Build features, training datasets and search indexes powering predictions and AI recommendations
- Build models and pipelines for customer dashboards, stakeholder reports, exports and internal analytics
- Implement automated data-quality checks, lineage and access controls
- Own pipelines from design through production, including data contracts and freshness and quality targets
- Partner with ML engineers on features and datasets and with full-stack engineers on product data
- Design pipelines that retry safely, can be rerun without duplicating data, and recover from bad inputs
- Add monitoring and observability so data problems are caught before customers notice
- Control costs as data volumes grow by selecting effective, simple tools
- Help establish data standards and shape the data platform as an early data engineer
Requirements
What you’ll need- 5+ years of experience building and running production data pipelines, with ownership from source to consumer
- Strong Python and advanced SQL, including writing and tuning complex queries
- Deep data modeling experience in Postgres or a similar relational database, including schema design, indexing, partitioning and migrations
- Experience parsing and normalizing messy real-world data, including large files, inconsistent formats, and missing or contradictory values
- Hands-on experience with workflow orchestration for multi-step pipelines with dependencies, retries and backfills
- Experience with AWS data services such as S3, managed databases, streaming and serverless compute
- Testing mindset for data, including validation, quality checks, and reproducible, idempotent pipelines
- Clear communication and comfort making pragmatic trade-offs on a small team
- Bonus: experience preparing data for ML, including feature pipelines, training datasets, and offline/online data consistency
- Bonus: experience with embeddings and vector search, such as pgvector or a search engine
- Bonus: event-driven data, including change data capture, streaming platforms, and the outbox pattern
- Bonus: familiarity with project scheduling data or tools from construction, engineering or energy
- Bonus: experience with regulated enterprise or public-sector data, including SOC 2, GDPR and data residency
- Bonus: infrastructure as code and CI/CD for data pipelines
- Bonus: early-stage or founding-team experience
- Bonus qualifications are not required
Benefits
Comp & perks- Health insurance
- Dental insurance
- Vision insurance
- 401(k)
- PTO
- Occasional travel may be required
- Equal opportunity employer