We are looking for a Data Engineer with strong expertise in Databricks, PySpark, SQL, and Azure Data Factory (ADF) to design and optimize scalable data pipelines. Experience with Snowflake and DBT is a plus. The ideal candidate will have a proven track record in building efficient ETL/ELT processes, data warehousing, and cloud-based data solutions.
Key Responsibilities:
Design, develop, and maintain ETL/ELT pipelines using Azure Data Factory (ADF) and Databricks.
Process and transform large datasets using PySpark (DataFrames, Spark SQL, optimizations) in Databricks.
Write and optimize complex SQL queries for data extraction, transformation, and loading.
Implement data lakehouse architectures using Delta Lake in Databricks.
(Optional) Manage and optimize Snowflake data warehouses (table structures, performance tuning).
(Optional) Use DBT (Data Build Tool) for modular and scalable data transformations.
Ensure data quality, governance, and monitoring across pipelines.
Collaborate with data analysts and business teams to deliver actionable insights.
Qualifications:
4+ years of hands-on experience in Databricks, PySpark, SQL, and ADF.
Strong expertise in Databricks (Spark, Delta Lake, Notebooks, Job Scheduling).
Proficiency in Azure Data Factory (ADF) for pipeline orchestration.
Experience with data warehousing concepts (ETL vs. ELT, dimensional modeling).
(Good to Have) Familiarity with Snowflake (warehouse management, Snowpipe).
(Good to Have) Knowledge of DBT (Data Build Tool) for transformations.
(Bonus) Python scripting for automation and data processing.
Preferred Qualifications:
Certifications (Databricks Certified Data Engineer, Azure Data Engineer).
Experience with CI/CD pipelines (Azure DevOps, GitHub Actions).
Knowledge of streaming data (Kafka, Event Hubs) is a plus.
Job Classification
Industry: Software ProductFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Data Platform EngineerEmployement Type: Full time