Architect and implement scalable ETL/ELT pipelines and services using modern data platforms and best practices.
Build streaming and micro-batch data flows, including schema evolution, late/out‑of‑order events handling, and exactly‑once delivery semantics where feasible.
Model data for analytics and ML using layered “bronze/silver/gold” patterns, with clear data contracts, SLAs, and lineage.
Embed observability (logging, metrics, tracing), data quality checks, and cost/performance optimization into everything you ship.
Automate testing and deployments with CI/CD
Collaborate with domain SMEs and data product owners to define requirements, acceptance criteria, and success metrics.
Operate what you build: participate in on‑call/incident response rotations and drive RCA and preventative engineering.

Requirements

BS in Computer Science, Engineering, or equivalent experience.
5+ years building data pipelines at scale with a modern data stack.
Strong proficiency in Python and SQL, plus performance tuning of both.
Hands-on experience with distributed compute (e.g., Apache Spark) and lakehouse/warehouse paradigms.
Data modeling for analytics (dimensional/medallion), data contracts, and schema management.
CI/CD (Git-based workflows) and infrastructure-as-code (e.g., Terraform) in a cloud environment.
Practical knowledge of data security, privacy, and access control concepts.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

ETLELTPythonSQLApache Sparkdata modelingCI/CDinfrastructure-as-codeperformance tuningdata quality checks

Soft Skills

collaborationcommunicationproblem-solvingincident responserequirements definitionacceptance criteriasuccess metricspreventative engineering

Certifications

BS in Computer ScienceEngineering degreeequivalent experience