Data Center Project Manager

Saxon Global•Dallas, TX

12h

About The Position

The posting seeks two senior contractors for a six-month engagement to develop, validate, and roll out enterprise data migration utilities and analytics pipelines. The work centers on building Python- and Java-based ETL/ELT pipelines that ingest, format, and harmonize data from multiple golden source systems into Databricks Lakehouse and Delta Lake. The role also develops downstream analytics and business intelligence reporting and involves cross-functional collaboration to gather requirements, validate data, and operationalize solutions for production. The position combines software engineering and data engineering. It covers pipeline and migration utility development, Databricks data modeling and BI enablement, integration testing and reconciliation across legacy and target platforms, CI/CD deployment, production monitoring, troubleshooting, and tier-3 support during pilot and cutover phases.

Requirements

Six to eight or more years of progressive software engineering and data engineering experience in enterprise environments.
Proven experience architecting or implementing legacy-to-cloud data migration utilities and multi-source ETL pipelines.
Demonstrated experience in both backend engineering and data engineering.
Deep hands-on experience with Databricks, including Delta Lake, Unity Catalog, Databricks Workflows/Jobs, and Spark SQL.
Expertise in distributed compute optimization, partitioning, vacuuming, and caching strategies.
Advanced Python proficiency, including PySpark, data-wrangling libraries, and modular object-oriented scripting for automated migration utilities.
Strong Core Java proficiency and enterprise backend development experience, including Spring Boot, REST APIs, or batch integration frameworks.
Advanced SQL and PL/SQL skills, including complex queries, window functions, query tuning, and index optimization across relational databases such as Oracle, SQL Server, and PostgreSQL.
Strong working proficiency with GitHub, including branching strategies, pull request reviews, and merge management.
Experience configuring and deploying code through automated CI/CD pipelines.
Strong diagnostic ability to identify bottlenecks across application code, network I/O, database locks, and cloud compute clusters.
Exceptional technical communication skills and ability to align platform teams, business analysts, and systems engineers.
Methodical approach to data quality, reconciliation frameworks, edge-case testing, and data integrity audits.
Proactive troubleshooting and ownership, self-directed delivery in an agile sprint rhythm, and readiness to support milestone cutovers.

Nice To Haves

Hands-on exposure to Kubernetes or Docker for microservices and utility hosting.
Familiarity with Event Hub, Kafka, or message broker patterns for near-real-time ingestion.
Basic understanding of or prior experience with Angular UI or web interfaces.
Practical experience delivering reporting models in Databricks SQL, Tableau, or Power BI.

Responsibilities

Design, build, and optimize enterprise-grade ETL/ELT pipelines and migration utilities using Python, PySpark, Pandas, Java, and related frameworks.
Extract, transform, and clean structured and semi-structured data from diverse source databases and enterprise applications.
Ingest and catalog data in Databricks Delta Lake while maintaining performance, data integrity, and compliance with data architecture standards.
Build semantic data layers, views, and aggregate tables in Databricks to support dashboards and operational reporting.
Develop and deliver analytical reporting solutions using Databricks SQL Dashboards, Power BI, or equivalent BI tools.
Gather requirements, schema definitions, and validation criteria in partnership with source system owners, interface teams, and platform engineering groups.
Coordinate end-to-end integration testing, data reconciliation, and sign-offs between legacy source platforms and modern target platforms.
Operationalize pipelines with error handling, automated retry logic, logging, data monitoring, and alerting.
Package and deploy code through enterprise CI/CD workflows.
Troubleshoot complex issues across application, middleware, data lakehouse, and infrastructure layers.
Provide tier-3 escalation support and issue remediation during pilot and cutover phases.