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Description
We are seeking an experienced Data Engineer with deep expertise in ETL/ELT, data architecture, and applied ML pipelines to design, build, and operate this infrastructure. As a key member of the team, you will be responsible for creating the massively scalable pipelines that turn raw data into a trusted foundation, driving critical decision-making and operations across the entire system.
Minimum Qualifications
Masters Degree 10+ years of experience in data engineering, including building and maintaining large-scale ETL/ELT data pipelinesProficiency in data modeling, especially dimensional modeling, and designing schemas optimized for analytics and reportingExperience with leveraging databases including SQL/NoSQL Databases (including Postgres / Cassandra / Redis)Strong experience with distributed data processing frameworks including Apache Spark Strong experience with Parallel processing frameworks: BigTable/HadoopStrong software engineering fundamentals and proven experience with Scala, JavaHands-on experience with Apache Kafka, Iceberg, and Flink. Experience with workflow orchestration tools including Apache Airflow and BeamExperience with AWS: e.g., S3, EMR, Lambda, Glue, Redshift, BigQuery, Kinesis, or similar servicesExperience with Analytics frameworks including Trino (Presto, BigQuery, Snowflake)Hands-on experience with big data lake architecturesExperience with containerization and orchestration (Docker, Kubernetes/EKS) and CI/CD tooling including Jenkins Experience in Python and PySparkFamiliarity with graph databases such as TigerGraphExperience building pipelines that process multimodal data (structured and image) and integrate ML model inference - including LLMs and embedding models - for data enrichment and transformationHands-on experience deploying, serving, and optimizing LLMs or ML models directly in the production, inference runtimes/compilers (ONNX Runtime, TensorRT/TensorRT-LLM), and serving frameworks (Triton, vLLM, TorchServe or similar). Experience tuning batching, KV-cache, and GPU utilization for low-latency, high-throughput real-time inference in a data pipelineKnowledge of data governance principles, data security best practices, and data privacy regulations
Preferred Qualifications
Experience with data versioning tools and frameworks (e.g., DVC, Delta Lake)Excellent communication skills and a collaborative mindsetExperience storing/serving embeddings (e.g., pgvector, Milvus, FAISS)
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