Lead Full Stack Engineer Engineer*

InfoBeans is hiring an experienced Senior / Lead Data Engineer to design, build, and optimize enterprise‑scale data platforms. The ideal candidate has strong hands‑on expertise with Azure Databricks, PySpark, ADF, Delta Lake, and modern data warehousing. In this role, you will work closely with architects, analysts, and business stakeholders to deliver high‑performance, reliable, and scalable data solutions.

If you thrive in fast‑paced environments and enjoy solving complex data challenges, we’d love to connect.

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Senior / Lead Data Engineer (Databricks) Experience: 10+ years Contract: C2H / W2 / 1099 Duration: 12 months Job Description Advanced Spark Optimization - Optimizing DataFrame transformations to avoid shuffles and wide dependencies, Adaptive Query Execution (AQE) tuning, Handling skew, Caching & checkpointing strategies, Cluster sizing & autoscaling strategies Delta Lake Internals & Performance Tuning - Optimizing Delta performance, Understanding Delta logs & transaction protocol , Time travel & schema evolution best practices , CDC (Change Data Capture) patterns , Multi-hop architecture (Bronze/Silver/Gold) Databricks Workflows & Orchestration - Orchestrating ETL/ELT with Jobs & Workflows, Multi-task job pipelines (with task dependencies) , Job clusters vs all-purpose clusters, Error handling & retries, CI/CD deployment using repos Unity Catalog & Enterprise Data Governance - Fine-grained access control (schemas, tables, columns, views), Data lineage tracking, Secure data sharing across workspaces, Managing tokens, service principals & permissions models Databricks SQL & Lakehouse Warehouses - Writing high-performance queries on the Lakehouse, Materialized views, SQL dashboards, Understanding Photon execution engine for high-speed queries Streaming & Real‑Time Pipelines - Structured Streaming internals, Auto Loader for incremental ingest, Trigger types & watermarking, Handling late-arriving data, Stateful stream processing Advanced Cluster & Compute Management - Spot vs on-demand clusters, Photon runtime vs standard , Cluster policies for cost control, SQL warehouses vs all-purpose compute, Monitoring with Ganglia / metrics dashboards Best Practices in Lakehouse Architecture - Medallion Architecture patterns, Modular, reusable ETL code patterns, Cost optimization strategies, Data quality frameworks (e.g., expectations, constraints) DevOps & CI/CD Integration - Git integration via Databricks Repos, Promoting code across dev/test/prod, Using the Databricks CLI, Automated deployments using Azure DevOps/GitHub Actions Nice To Have Azure Data Factory - Understanding of Pipelines, Activities, and Datasets , Linked Services configuration , Integration Runtime types (Auto‑IR, Self‑Hosted IR, Azure IR) , Source and Sink concepts in ADF ETL/ELT Data Integration - Building data ingestion pipelines (batch + incremental loads) , Designing end‑to‑end data transformation