Principal Software Engineer - Analytics Data

d distributed processing frameworks integrated with Airflow for orchestration

• Champion Apache Iceberg-based data lakehouse adoption (versioning, branching, WAP, indexing)

• Introduce performance optimization patterns for data pipelines (e.g., partition pruning, caching, resource tuning)

• Establish patterns for metadata-driven, intent-based data processing aligned with AI-assisted pipelines

Architecture & Delivery

• Partner with architects to design multi-tenant, secure, and compliant (SOC2/GDPR/CCPA) batch processing services

• Define reference architectures and reusable frameworks for cross-domain processing workloads

• Lead technical reviews, solution designs, and architectural forums across ADP

• Guide the evolution of Airflow APIs and SDKs to enable scalable pipeline self-service

Mentorship & Collaboration

• Mentor senior engineers, staff engineers, and tech leads within the Batch Processing and adjacent teams

• Partner with the Batch Ingestion and Data Security teams to deliver unified ingestion + processing flows

Innovation & Impact

• Drive modernization initiatives to migrate away from legacy tools

• Pioneer AI-augmented data engineering practices (e.g., pipeline optimization agents, anomaly detection)

• Ensure scalability, cost-efficiency, and reliability for thousands of production pipelines across Autodesk

• Influence company-wide data engineering strategy by contributing thought leadership and whitepapers

Minimum Qualifications

• 8+ years of experience in software/data engineering, with at least 3 years in big data platform leadership roles

• Expert in distributed data processing (Spark, PySpark, Ray, Flink)

• Deep experience with workflow orchestration (Airflow, Dagster, Prefect)

• Strong hands-on expertise in Lakehouse technologies (Iceberg, Delta, Hudi) and cloud platforms (AWS/Azure/GCP)

• Proven track record in architecting secure, multi-tenant, and compliant data platforms

• Skilled in SQL/NoSQL databases, Snowflake, Glue, and modern metadata/catalog tools

• Strong problem-solving, communication, and cross-geo collaboration skills

• Experience mentoring engineers, building strong technical culture, and influencing at scale

Preferred Qualifications

• Exposure to AI/ML-driven data engineering (optimizers, anomaly detection, auto-scaling)

• Experience with data governance, lineage, and observability tools (Atlan, Databand, Collibra, etc.)

• Familiarity with streaming + batch hybrid architectures (Kappa/Lambda)

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