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Remote role, popular mid-level data engineer title, and metro locations drive high applicant density.
Core data engineering skills are transferable, though SaaS product and big-data tools create moderate domain specificity.
Specific 5+ years requirement and extensive mandatory big-data and cloud toolset increases filter strictness.
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Design, build, and maintain scalable, high-availability data pipeline architectures integrating various data sources and supporting big data velocity and variety.
Translate business requirements into technical data specifications, while defining data architecture standards including modeling, security, metadata, and master data.
Ensure data quality, security, compliance, and support cross-functional teams with data infrastructure needs, including enabling AI access to data.
4-12 years work experience in a Data Engineer role with SaaS/B2C product development for external customers.
Graduate degree in Computer Science, Statistics, Informatics, Information Systems, or related quantitative field.
Strong SQL expertise and experience with big data tools (Hadoop, Spark, Kafka), relational and NoSQL databases (Postgres, Cassandra), and cloud services (AWS).
Experience with data pipeline/workflow tools (Azkaban, Luigi, Airflow) and programming languages like Python or Java.
Experienced in designing and implementing complex data architectures focused on scalability, data integrity, and availability.
Skilled in cross-team collaboration including Product, Data Science, and AI teams to enable data-driven product innovation.
Proficient with cloud-based big data ecosystems and tools, with strong analytical skills to work with unstructured and large datasets.