Data Platform Manager- Hybrid (Applicants must reside in Pittsburgh, PA)

A.C. Coy; United States; Hybrid; USD 120,000 - 190,000; Full time

Job summary

A.C Coy is seeking a Data Platform Manager to lead a data engineering team and architect enterprise-grade data platforms within a Lakehouse framework. The role focuses on designing, building, and optimizing pipelines in Azure Databricks, ensuring data quality, lineage, and governance.

You will collaborate with data scientists, analysts, and stakeholders to define requirements, implement scalable batch and streaming ETL/ELT processes, and align with security and cost optimization in the Azure

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 5-7+ years hands-on data engineering or architecture experience, with at least 2-4 years on Azure Databricks.
• 2-5 years experience managing a team of data engineers, data scientists and/or analysts.
• Certifications preferred: DP-203, Databricks Certified Data Engineer, or Azure Solutions Architect Expert.
• Proficiency in Relational and NoSQL databases; data modeling and schema design.
• Experience with Spark, Hadoop, Spark SQL, PySpark, SQL and Azure cloud services.

Responsibilities

• Lead and mentor a team of data engineers, conducting code reviews and ensuring development standards.
• Support troubleshooting and incident management for data-related issues in production.
• Collaborate with stakeholders, data scientists and analysts to translate requirements into technical specs.
• Lead design, development and deployment of scalable data pipelines using Azure Databricks; manage ETL/ELT with low latency.
• Collaborate to operationalize ML models and deliver business-critical insights.
• Define best practices for data ingestion, processing, storage, governance, quality checks and lineage.

Skills

Team leadership

Mentoring

Requirements gathering

Stakeholder collaboration

Incident management

Data governance

Data modeling

Education

Bachelor's degree in CS/Engineering or related field

Azure Data Engineer DP-203

Databricks Certified Data Engineer

Azure Solutions Architect Expert

Tools

Azure Databricks

Azure Cloud

Apache Spark

Apache Hadoop

Airflow

Delta Lake

Job description

Overview

Location: Hybrid, Pittsburgh, PAJob Type: Full Time / PermanentWork Authorization: No C2C or Sponsorship

The A.C.Coy company has an immediate opening for a Data Platform Manager. This role will be responsible for designing, building, and optimizing enterprise wide data platforms within the Data Warehouse.

Responsibilities

• Lead and mentor a team of data engineers, conducting code reviews and ensuring development standards


• Support troubleshooting and incident management for data-related issues in production


• Collaborate with business stakeholders, data scientists, and other team members to gather requirements and translate them into technical specifications


• Lead the design, development and deployment of scalable and high-performance data pipelines using Azure Databricks; ensuring the data integrity, availability, efficient extraction, transformation, and loading of data from various sources into the Azure Databricks Data Warehouse


• Collaborate with data scientists, analysts, and other engineering teams to deliver business-critical insights. Optimize pipeline performance, cost, and scalability in the Azure cloud environment


• Define best practices for data ingestion, processing, storage, and governance. Implement data quality checks and validation procedures to ensure the accuracy and integrity of data between various sources, including APIs, databases and streaming platforms


• Collaborate with data scientists and analysts to operationalize and deploy machine learning models


• Architecture Design: Define the end-to-end Lakehouse architecture using Delta Lake, implementing medallion architecture (Bronze, Silver, Gold layers) for robust data processing


• Familiarity with data modeling and schema design principles


• Pipeline Engineering: Oversee the development of robust, scalable batch and streaming ETL/ELT pipelines using PySpark, Scala, and SQL and with minimal latency


• Implement data transformations, enrichment, and quality checks using PySpark/Scala within the Databricks environment


• Integrate real-time and batch data sources using Apache Kafka and ADF


• Support large-scale data pipelines using Apache Spark on Databricks, Kafka, Stelo, and Azure Data Factory (ADF)


• Data Governance & Security: Implement Unity Catalog for unified governance, data security, fine-grained access control (RBAC), privacy measures, and data lineage tracking


• Performance Optimization & Tuning: Tune Spark jobs and Databricks clusters to maximize throughput while maintaining cost efficiency through auto-scaling and cluster policies


• Expertise in indexing strategies, query optimization, execution plans, and partitioning/sharding


• Platform Integration: Orchestrate workflows by integrating Databricks with other Azure services like Azure Data Factory (ADF), Azure Data Lake Storage (ADLS Gen2), and Azure DevOps for CI/CD pipelines

Qualifications

• Required Education
  Bachelor's degree in Computer Science, Engineering, or a related field


• Required Experience
  5-7+ years hands-on data engineering or architecture, with at least 2-4 years specifically focused on Azure Databricks, including Azure cloud technologies


• 2-5 years experience in managing a team of data engineers, data scientists and/or analysts


• Certifications (Preferred): Microsoft Certified: Azure Data Engineer Associate (DP-203), Databricks Certified Data Engineer Professional, or Azure Solutions Architect Expert


• Database Architecture: Proficiency in both Relational (SQL) and NoSQL (Document, Key-Value, Graph, Columnar) databases. Develop and maintain data models and schemas to support data analysis and reporting requirements


• Distributed Systems: Knowledge of frameworks like Apache Hadoop, Spark, or Presto/Trino for optimizing and handling massive data volumes and retrieval mechanisms, ensuring the efficient processing of large datasets


• Storage Optimization: Understanding file formats like Parquet, Avro, or ORC and compression techniques


• Deep proficiency in programming languages: Python (specifically PySpark), SQL, PowerShell, and Scala


• Infrastructure: Hands-on experience with Azure Cloud infrastructure, including Networking (VNETs), Key Vault, and Identity Management


• Big Data Tools: Deep knowledge of Apache Spark runtime internals, MLflow for MLOps, and orchestration tools like Airflow