Data Engineer

Job Overview:

Pay Range: $50.00hr - $55.00hr

Requirement/Must Have:

• 5+ years building batch and/or streaming pipelines with Spark.
• Deep understanding of Spark fundamentals including lazy evaluation, transformations vs. actions, partitions, shuffles, joins, and caching.
• Experience with Databricks platform on Azure including Unity Catalog (catalogs/schemas/tables, permissions, external locations, storage credentials, lineage).
• Proficiency with Databricks Jobs/Workflows for task orchestration, cluster policies, retries, and parameters.
• Experience with Git integration (Repos), CI/CD, and environment promotion.
• Knowledge of cloud storage and access on Azure, specifically secret management via Databricks secret scopes and Key Vault integration.
• Experience with Delta Lake including ACID transactions, time travel, MERGE/UPDATE/DELETE, and schema enforcement/evolution.
• Strong SQL skills including complex SQL with CTEs and window functions.
• Experience with data modeling and architecture using Medallion (bronze/silver/gold) patterns for data lakes.
• Ability to design tables for CDC and incremental processing.
• Experience with large scale data processing including cluster sizing, autoscaling, and cost optimization.
• Familiarity with observability tools such as Spark UI, metrics, event logs, and Delta logs.
• Knowledge of software engineering practices including Python packaging, code quality, and testing (pytest, chispa).
• Strong verbal and written communication skills.

Responsibilities:

• Build and maintain batch and streaming data pipelines using Spark and PySpark.
• Manage Databricks platform components including Unity Catalog and Workflows.
• Implement data modeling strategies such as partitioning and file layout to match read patterns.
• Ensure data governance, security, and compliance within the Databricks environment.
• Execute CI/CD processes for Databricks using Repos and asset bundles.
• Collaborate with stakeholders and provide technical documentation.

Nice to Have:

• Performance tuning: broadcast joins, AQE, skew mitigation, and shuffle optimization.
• Structured Streaming and Auto Loader for incremental ingestion.
• Declarative pipelines with Delta Live Tables (DLT).
• ADLS Gen2: ABFS access, ACLs vs. RBAC, and managed identity.
• OPTIMIZE, Z ORDER, VACUUM and retention policies.
• Compaction and small files mitigation strategies.
• Query optimization and execution plan analysis in Databricks SQL.
• Lakehouse design trade-offs and table constraints.
• Handling TB scale datasets and throughput vs. latency trade-offs.
• Privilege model in Unity Catalog; table, row, and column level security patterns.
• PII handling, masking/anonymization, and auditability.
• Scala, dbt, Kafka/Event Hubs, ADF/Airflow, Terraform, Power BI, data mesh concepts, and SCD patterns.

Skills:

• Spark.
• PySpark.
• Azure Databricks.
• Unity Catalog.
• Delta Lake.
• SQL.
• Python.
• Azure Data Lake Storage (ADLS).
• CI/CD.
• Data Modeling.
• Java.

Qualification And Education:

• 10-12 years of professional experience in data engineering or related fields.
• Extensive experience with the Azure cloud ecosystem.