Data engineering that holds up in production

Dheefinity designs and builds data platforms that enterprises run their businesses on — not prototypes.

Capabilities

• Data lake and lakehouse architecture on Azure (ADLS Gen2, Databricks, Data Factory, Synapse), AWS (S3, Lambda, Redshift), and GCP (BigQuery, DataProc)

• Spark at expert level: Unity Catalog, Structured Streaming, SparkSQL, MLlib, performance optimization — in Python and Scala. Databricks Certified Spark Developer.

• Streaming and ingestion: Kafka, NiFi, event-driven pipelines

• Secure-by-design: KeyVault-protected secrets, Kerberos, SSL, handling PII data

Selected engagements

ERP data migration, leading US grocery retailer: Led design and build of a secure ADLS Gen2 data lake and ELT pipelines converting legacy data to Infor M3, including team mentoring and release process setup. (Azure Databricks, PySpark, ADF)

Enterprise data-access analytics, top-5 Canadian bank: Built the system that collects and publishes aggregate views of how applications and people access data assets across the enterprise. (Spark, Scala, Hive, Airflow)

Client profitability data lake, major US bank: Spark-based expense-allocation engine processing client revenues, trading volumes, and regional expense data. (PySpark, Hive, Cloudera)

Flexible-format ingestion, major Canadian pension fund: Scala DSL plus AWS Lambda pipeline ingesting hundreds of changing company-report formats into Redshift. (Scala, AWS Lambda, Redshift)

Engagement model: senior, hands-on, accountable. The person who architects the solution writes the code.

[Book a call] · kumar.vn@dheefinity.com