Engineer III - Data Engineer

Responsibilities

• Data Pipeline Orchestration: Design, build, and run batch and scheduled data pipelines (Airflow, Dagster, Prefect) and lead orchestrator upgrades.
• Data Ingestion Scraping: Own and extend data collectors and engines, including web/API ingestion, large-scale web scraping (Playwright, Selenium, Scrapy), parsing, normalization, and file merging.
• Distributed Processing: Build scalable distributed processing architectures (Spark on EMR, Data bricks, DataProc) and analytical query layers (Athena, Snowflake, BigQuery), including cross-cloud ingestion pipelines.
• Caching Entity Matching: Improve entity matching and caching services (Redis, MemoryDB, DynamoDB) to optimize accuracy, latency, and operational cost.
• Data Access APIs: Develop and maintain high-performance Python data access layers and APIs for downstream ML and product teams.
• Data Quality Observability: Own data quality initiatives, including validation frameworks, retention policies, backfills, monitoring, and OpenTelemetry-based observability.
• Cross-Functional Collaboration: Partner with data science, forecasting, and platform teams to ship features safely behind feature flags (LaunchDarkly, Unleash).
• Engineering Leadership: Raise engineering standards across the team through rigorous code reviews, technical design documents, and mentoring.

What you bring

• Experience: 6+ years of software or data engineering experience, including 3+ years of experience building and maintaining production data pipelines.
• Python Mastery: Strong Python development skills with a commitment to writing clean, tested code (using pytest, nox, or tox).
• Orchestration Expertise: Deep experience with workflow orchestration (Airflow DAG design, operators, scheduling, and failure handling) or modern alternatives like Dagster and Prefect.
• Data Ecosystems: Hands-on experience with distributed compute (Spark) and cloud data ecosystems (AWS EMR/Athena/S3, Data bricks, Snowflake, GCP).
• Storage Modeling: Solid SQL and data-modeling skills, alongside practical experience with NoSQL databases, caching layers, and message queues (MongoDB, Redis, Kafka, RabbitMQ).
• Infrastructure Familiarity: Comfort deploying and managing containerized workloads on Kubernetes using Docker and Helm.
• Production Ownership: A proven track record of owning production pipelines, including participating in on-call rotations, incident response, and root-cause analysis.
• Communication: Clear written technical communication and a history of documenting architectural decisions.

Nice to have

• Scraping Infrastructure: Experience managing large-scale web data collection and distributed crawler infrastructure.
• Data Science / ML Concepts: Familiarity with record linkage, entity resolution, fuzzy matching, and NLP preprocessing (spaCy, NLTK).
• Multi-Cloud Pipelines: Hands-on experience building multi-cloud data pipelines (e.g., bridging GCP/BigQuery and AWS).
• ML Ops: Experience designing data pipelines that directly feed machine learning or forecasting systems.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.