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Roles and Responsibilities
- Build and maintain scalable, fault-tolerant
data pipelines
to support GenAI and analytics workloads across OCR, documents, and case data. - Manage ingestion and transformation of semi-structured legal documents (PDF, Word, Excel) into structured formats.
- Enable
RAG workflows
by processing data into chunked, vectorized formats with metadata. - Handle large-scale ingestion from multiple sources into
cloud-native data lakes
(S3, GCS),
data warehouses
(BigQuery, Snowflake), and PostgreSQL. - Automate pipelines using orchestration tools like
Airflow/Prefect
, including retry logic, alerting, and metadata tracking. - Collaborate with ML Engineers to ensure data availability, traceability, and performance for inference and training pipelines.
- Implement data validation and testing frameworks using
Great Expectations
or
dbt
. - Integrate OCR pipelines and post-processing outputs for embedding and document search.
- Design infrastructure for
streaming vs batch
data needs and optimize for cost, latency, and reliability.
Qualifications
- Bachelor's or Master's degree in Computer Science, Data Engineering, or equivalent.
- 3+ years of experience in building distributed data pipelines and managing multi-source ingestion.
- Proficiency with
Python
,
SQL
, and data tools like Pandas, PySpark. - Experience working with data orchestration tools (Airflow, Prefect), and file formats like Parquet, Avro, JSON.
- Hands-on experience with cloud storage/data warehouse systems (S3, GCS, BigQuery, Redshift).
- Understanding of GenAI and vector database ingestion pipelines is a strong plus.
- Bonus: Experience with OCR tools (Tesseract, Google Document AI), PDF parsing libraries (PyMuPDF), and API-based document processors.