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Recroots · posted 3 days ago
Own the architecture and evolution of our core data platforms , spanning Lakehouse infrastructure , analytical pipelines , and transactional systems .
Design and build scalable data ingestion and transformation pipelines ( batch and streaming ) with a strong emphasis on data quality , reliability , and downstream usability .
Define data modeling standards across our stack , including dimensional , medallion , and schema - first patterns .
Drive performance optimization across SQL and NoSQL systems , with a focus on query efficiency , partitioning strategies , and throughput at scale .
Collaborate closely with API and Data Science teams to build robust pipelines and shared data contracts .
Establish and enforce data security practices , including encryption boundaries , access controls , and tenant isolation .
Set standards for observability across data systems using tools like Grafana , cloud watch , and OpenTelemetry .
Mentor engineers across the org and lead architectural decisions through review and documentation .
8+ years of experience in data engineering or a closely related discipline , with a focus on large - scale distributed systems .
Hands - on experience with Databricks and lakehouse architecture patterns .
Strong understanding of OLAP systems and experience designing star schema and galaxy schema data models .
Strong understanding of transactional systems design , including consistency models , write patterns , and operational reliability .
Expertise in data modeling across both SQL and NoSQL ecosystems .
Strong programming skills in Python and Spark with an emphasis on clean , maintainable code .
Hands - on experience with AWS services including S 3, Lambda , and IAM .
Familiarity with observability stacks ( Grafana , Cloudwatch ) and experience debugging production bottlenecks .
Knowledge of DynamoDB and PostgreSQL is a plus .
Excellent communication skills and a track record of influencing cross - functional technical decisions