Senior Data Engineer

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Seamless

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1999

🍽️ Food & Beverage

👥 B2C

🏪 Marketplace

💰 Venture Round - Seamless on 2011-12

Food & Beverage • B2C • Marketplace

Seamless is a consumer-facing food delivery platform. It operates as an online service for ordering prepared meals from restaurants for delivery or pickup; availability may be limited by country or region.

📋 Description

• Build and maintain PySpark ETL jobs • Support ML-based scoring models predicting contact-data quality and validity • Write and optimize SQL for large silver/gold lake-table joins • Collaborate on scoring-system architecture, including rule-based gating and ML positioning • Write unit and integration tests for data transformations and model inference • Work within a bronze/silver/gold medallion lake architecture • Identify opportunities for AI and agentic workflows in data acquisition, identity resolution, and profile aggregation • Investigate data-quality issues and analyze scoring-rule outcomes • Own architecture and projects independently from analysis and planning through implementation, testing, and production • Trace data lineage and debug execution across distributed AWS services • Document data-flow findings and architecture decisions

🎯 Requirements

• 5+ years of professional data engineering experience • End-to-end project ownership from design through production • Ability to make and defend architecture decisions with minimal oversight • Strong Python skills, including PySpark DataFrames • Deep hands-on Apache Spark/PySpark experience, including partitioning, shuffles, skew, broadcast joins, predicate/partition pruning, caching, and physical query plan analysis • Complex SQL fluency: multi-way joins, window functions, CTEs, and aggregations at billion-row scale • Experience with AWS Glue, EMR, S3, Step Functions, Lambda, EventBridge, and CloudWatch • Experience writing automated data-pipeline tests using pytest or similar • Ability to trace data lineage across bronze/silver/gold pipeline stages • Orchestration experience with Step Functions, Airflow, or similar DAG-style workflows • Experience modeling and deduplicating heterogeneous upstream data into canonical schemas • Ability to debug distributed AWS services using CloudWatch logs • Exploratory data analysis at scale • Understanding of noisy ground-truth proxies and score-quality implications • Knowledge of classification statistics, including precision/recall, error-cost tradeoffs, and calibration • Backtesting and historical validation experience • Root-cause analysis of data-quality issues • Ability to translate ambiguous goals into testable criteria • Data validation discipline involving row counts, cardinality, output diffs, data profiling, and silent failure modes • Experience with automated data-quality checks • Must be authorized to work in the U.S. • Nice to have: classical ML model development, scikit-learn, contact-data quality, identity resolution, marketing/sales enrichment, CI/CD, EMR, blended rule/ML scoring systems, and LLM/agentic workflows

🏖️ Benefits

• Artificial intelligence and cutting-edge technology work • Equal opportunity employer • Visa sponsorship is not included