Design and implement scalable and efficient data architectures to support business needs.
Collaborate closely with data scientists, analysts, and cross-functional teams to build and optimize data pipelines.
Develop and maintain data models, databases, and data lakes
Implement robust data governance and quality assurance practices.
Transform raw time-series data into ML-ready features, training datasets, and batch predictions.
Create reproducible workflows, reliable feature datasets, and batch prediction pipelines.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
6+ years in data engineering, platform development, or large-scale data systems.
Hands-on experience with Databricks or modern lakehouse platforms and cloud platforms (AWS, GCP, or Azure).
Experience building scalable ETL/ELT pipelines using Spark and SQL.
Proficiency in SQL and experience with NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB).
Strong understanding of data modeling, schema design, and performance optimization.
Experience building reliable, production-grade data pipelines with a focus on data quality and observability.
Experience supporting analytics and/or ML workflows, including preparing ML-ready datasets.
Working knowledge of data governance, security, and access control frameworks.
Familiarity with Infrastructure as Code (IaC) and automated deployment workflows (e.g., Terraform).
Proven ability to collaborate across teams and contribute to technical direction.

Benefits

Applicant Tracking System Keywords

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

Hard Skills & Tools

data architecturedata modelingETLELTSQLNoSQLSparkdata governancedata qualityfeature engineering

Soft Skills

collaborationcommunicationproblem-solvingtechnical direction

Certifications

Bachelor’s degreeMaster’s degree