We are seeking a Senior Data Software Engineer with Azure Databricks expertise to grow the data ingestion and curation capabilities of the client's Enterprise Data Lake (EDL) on Microsoft Azure and Databricks. The team will bring new data sources onto the lakehouse and curate them through the Medallion layers (Bronze → Silver → Gold), so the business has trusted data for reporting and analytics. The first project is API ingestion from Genesys Cloud (contact centre platform), starting with daily loads and moving to near real-time and streaming in later phases, with more sources to follow. This role will lead a team of data engineers and deliver designs set by the Solution Architect. Responsibilities Lead the engineering team: break designs (HDD/TDD) into tasks, distribute work, track delivery and remove blockers Turn designs into working pipelines and find the right technical solutions where a design leaves gaps Evolve reusable ingestion patterns (API, file, database, streaming) on the metadata-driven EDL framework Lead curation across the Medallion layers: append-only Bronze, SCD2 history in Silver, and support for Gold models built with data modelers Own the physical table design (liquid clustering vs. partitioning), the Unity Catalog structure and RBAC Lead data quality and reconciliation: source-to-lake checks, DQ rules, audit logging and alerting Drive performance improvements: find bottlenecks, optimize Spark jobs, Delta tables, SQL queries and Power BI models and reduce compute cost Guide the creation of Power BI reports and Databricks dashboards for business reporting and pipeline monitoring Review code, enforce engineering standards, and own CI/CD promotion (DEV → TST → PRD) Work with the Solution Architect, platform/CloudOps teams and business stakeholders, and prepare runbooks and the handover to operations Requirements 3+ years of experience in Big Data/data engineering on Azure Databricks, with a track record of leading delivery teams Experience leading teams and architecture: distributing tasks, code review, finding the right technical solutions and guiding the team to implement them Knowledge of Medallion Lakehouse architecture, covering both ingestion and curation Expertise in Azure Databricks: Workflows/Jobs, Notebooks, Unity Catalog (schemas, views, grants, service principals), SQL Warehouse Proficiency in Delta Lake: MERGE, SCD Type 2, OPTIMIZE and small-file handling Understanding of liquid clustering and partitioning: choosing clustering keys, data skipping, and the effect on MERGE performance Skills in Spark Structured Streaming: Auto Loader, checkpoints, triggers, watermarking, stateful processing, exactly-once delivery into Delta Proven track record of performance improvements: Spark tuning (Spark UI, shuffle and skew, joins, AQE), Delta file layout, SQL optimization, and compute right-sizing and cost Proficiency in PySpark, Python and strong SQL Experience in REST API ingestion: OAuth2, async jobs, pagination, rate and concurrency limits, retries and idempotent reloads Familiarity with Azure: Data Factory, ADLS Gen2, Key Vault, Azure SQL Database (config-driven frameworks) Background in data modeling, data quality and reconciliation, CI/CD for data platforms (GitHub, PR-based promotion), and ability to create Power BI reports and Databricks dashboards
