Job Title: Data engineer (Data Bricks)
Location: Onsite: 100% , Seattle WA, St louis, Dallas/ Plano, Charleston SC, Ridley Park Pennsylvania
Duration: 06 months (with possibility of extension)
Pay Rate: $68/hr on W2
(ITAR REQ)
This is a large migration project and needs experience and broader scope in various activities of migrations
Work breakdown
60% development
20% business funcion
20% production support
Must Have Technical/Functional Skills- Successfully executed a data migration or modernization to Data Bricks, preferably IBM
- Data Stage to Data Bricks on AWS
- Should have Experience in handling Large Migrations to Data Bricks.
- Should have good analytical skills to compare the legacy and modern data platform end to end right from source to target.
- Good understanding of DataBricks implementation of Medallion layer architecture.
- Independently Lead and Managed large Data Bricks migrations.
- CI/CD Integration: Implement version control (e.g., Git) and automated deploymen processes for Databricks assets
Technical and architectural skillsCore Data Engineering Languages
· Experience in Advanced SQL for building modular analytics workflows, utilizing advanced Common Table Expressions (CTEs), and writing high-performance queries inside Data Bricks SQL Analytics.
· Experience in Python or Scala to build, optimize, and debug complex data transformation scripts, custom functions, and machine learning pipelines.
Big Data & Architecture Core
· Experience in Apache Spark Ecosystem for understanding cluster execution flow, memory allocation, driver/worker nodes, and handling data frames.
· Experience in Delta Lake Architecture to understand ACID transactions on object storage, data skipping, partition strategies, and automated data compaction.
Databricks Platform Expertise
· Experience in Delta Live Tables (DLT) & Workflows for constructing and orchestrating production-ready, declarative streaming, and batch ETL pipelines.
· Experience in Unity Catalog for setting up data governance, column/row-level access control, and tracking end-to-end data lineage across workspaces.
· Experience in Auto Loader for implementing modern, incremental data ingestion patterns from cloud blob storage into the lakehouse.
Code Translation & Refactoring
· Pipeline Conversion: Translate visual DataStage Parallel Jobs and Sequences into Python/PySpark scripts or Data bricks Notebooks
· Legacy Refactoring: Modernize legacy logic rather than applying "lift and shift" anti- patterns; adapt workflows to think in distributed DataFrames rather than DataStage stages.
· Logic Mapping: Map DataStage components—such as Aggregators, Joiners, Transformers, and Sort stages—to equivalent Spark operations
Testing & Reconciliation
· Validation & Reconciliation: Build automated reconciliation frameworks to compare row counts, checksums, and aggregate sums between legacy DataStage outputs and new Databricks output
· Data Cleansing: Identify and resolve data type discrepancies, null-handling differences, and encoding issues during the extraction and loading phases
Platform Orchestration & Governance
· Orchestration: Replace DataStage sequence jobs with Databricks workflows ( or external orchestrators like Azure Data Factory/Airflow) to schedule and manage dependencies
· Data Governance: Enforce data lineage, security, and cataloging using Unity Catalog to ensure compliance in the new Lakehouse environment.
GOOD TO Cloud Infrastructure & CI/CD
· Cloud Providers (AWS): Understanding underlying cloud object storage , identity access management (IAM), and network security configurations.
· DevOps & Bundles: Familiarity with Databricks Asset Bundles (DABs) and CI/CD tools to automate the deployment of workspaces and pipeline assets.
Legacy Assessment & Migration Mechanics
· Code Conversion & Translation: The ability to parse legacy code structures and refactor them into Databricks-native code.
AI-Assisted Migration: Skills in using AI coding assistants and open framework agent tools to analyze application interdependencies, automate schema mapping, and accelerate lift-and-shift workloads
· Code Conversion & Translation: The ability to parse legacy code structures from ETL pipelines, Informatica, data Stage preferred
Experience working in Agile teams and understanding of data governance frameworks.
Responsibilities
Support post-migration environment from IBM DataStage to Databricks
Incident & Lifecycle Management
· CI/CD Deployment: Support code deployments across Development, Test, and Production environments using Databricks Repos and REST APIs
· Monitoring & Alerting: Set up monitoring via Databricks System Tables and observability tools to catch job failures, data anomalies, or latency spikes early
Pipeline Maintenance & Orchestration
· Workflow Management: Transition from DataStage job sequences to native data bricks workflows for scheduling, dependency tracking, and alerts
· ETL Refactoring: Troubleshoot and fix issues in generated PySpark or Spark SQL code that replaced legacy DataStage Transformer or Lookup stages
· Streaming & Batch Integration: Support ongoing data ingestion using data bricks autoloader to process files continuously from cloud storage
Performance Tuning & Cost Optimization
· Compute Management: Monitor and configure serverless or classic clusters to prevent over-provisioning
· Query Optimization: Analyze Spark execution plans. Replace inefficient row-by-row processing logic (a common DataStage carryover) with vectorized operations and native Spark functions
· Storage Optimization: Maintain Delta Lake tables by enforcing layout optimization (\(ZORDER\)
Data Governance & Security
· Access Control: Implement granular permissions, column-masking, and row-level filters using Data bricks unity catalog to replace DataStage's legacy security policies · Data Quality: Utilize Delta Live Tables (DLT) to build pipelines with built-in, declarative data quality expectations and monitoring
Additional Skills
· Excellent communication Skills
· Ability to collaborate with Legacy and Modernize application teams and stake holders