Role Summary
We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:
What You'll Own
- Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
- AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
- Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
- Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
- AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
- ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
- APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
- Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.
Key Skills — Non-Negotiable (Must-Have, Strong Level)
- Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
- SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
- AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
- Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
- ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
- Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).
Key Skills — Good to Have
- Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
- FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
- ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
- Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
- Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.
Shubham Vishwakarma
Full Stack Developer - Averlon
About J&F
About
At J&F, we are a globally established engineering consultancy with over 500 engineers and detailers across five countries, delivering high-quality engineering solutions for buildings and infrastructure projects. We specialize in BIM-enabled Structural and MEP engineering, combining deep technical expertise with digital innovation to help clients execute complex projects efficiently, accurately, and at scale.
Through our two core business lines—Virtual Technical Office and End-to-End Engineering Solutions—we provide tailored engineering support as well as fully integrated project planning and supervision. By partnering closely with our clients, we deliver sustainable, cost-effective, and technology-driven solutions across a wide range of sectors, ensuring engineering excellence throughout every stage of the project lifecycle.
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Full Stack Developer - Averlon