Lead Full Stack 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.

Below, you will find a complete breakdown of everything required of potential candidates, as well as how to apply Good luck.
If you thrive in fast‑paced environments and enjoy solving complex data challenges, we’d love to connect.
Title: Senior / Lead Data Engineer (Databricks)Experience: 10+ yearsContract: C2H / W2 / 1099Duration: 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 strategiesDelta 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 reposUnity 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 modelsDatabricks SQL & Lakehouse Warehouses - Writing high-performance queries on the Lakehouse, Materialized views, SQL dashboards, Understanding Photon execution engine for high-speed queriesStreaming & Real‑Time Pipelines - Structured Streaming internals, Auto Loader for incremental ingest, Trigger types & watermarking, Handling late-arriving data, Stateful stream processingAdvanced 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 dashboardsBest Practices in Lakehouse Architecture - xzbcwsv 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 ADFETL/ELT Data Integration - Building data ingestion pipelines (batch + incremental loads), Designing end‑to‑end data transformation