Engineer III - Data Engineer

Overview

Engineer III, Data Engineer role at Anaplan focused on building and running production data pipelines, scraping and ingestion, distributed processing, and data access APIs for downstream ML and product teams.

What you'll do

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

What you'll need

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

Nice to have

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

Details

• Location: Gurugram.