Google Cloud Platform Data Lead

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What You'll Do (Responsibilities)

Architect & Design

Design and implement robust, scalable, and cost-effective data solutions on Google Cloud, serving as the target architecture for migrated workloads.

Develop Reusable Frameworks & Accelerators

Design, build, and maintain reusable frameworks, templates, and code libraries to standardize and accelerate data engineering work.

This includes creating boilerplate pipeline structures, generic data validation modules, and automated deployment patterns that other engineers will leverage.

Migrate & Modernize

Lead the hands-on migration of data and processes from on-premises systems like Teradata and Hadoop to Google Cloud services, with a primary focus on BigQuery, Google Cloud Storage (GCS), Dataflow, and Dataproc

ETL/ELT Transformation

Analyze, deconstruct, and translate complex legacy ETL logic from tools like Informatica and Teradata BTEQ/Stored Procedures into modern, cloud-native pipelines, leveraging the frameworks/tooling you help create.

Pipeline Development

Build and automate new data pipelines for batch and streaming data using Python, SQL, and Google Cloud Platform's core services, ensuring all new development contributes to and benefits from our shared engineering frameworks.

Performance & Cost Optimization

Proactively optimize BigQuery performance through effective partitioning, clustering, and query tuning.

Data Validation & Governance

Develop and implement rigorous data validation frameworks to ensure data integrity and accuracy post-migration.

Collaborate with governance teams to apply data security, lineage, and cataloging using tools like Google Cloud Data Catalog and Dataplex.

Collaboration & Mentorship

Work closely with on-premises data experts, business analysts, and other engineers to understand requirements, ensure a smooth transition, and act as a subject matter expert and mentor for Google Cloud Platform and internal framework best practices.

Required Qualifications (Must-Haves)

Professional Experience: 5+ years of professional experience in a data engineering role, with a proven track record of building and maintaining large-scale data systems.

Framework Design & Development

Demonstrable experience designing, building, and promoting the adoption of reusable data engineering frameworks such as Ingestion, Transformation, Validation etc

On-Premises Data Warehouse/Big Data Expertise

Deep, hands-on experience with at least one of the following on-premise ecosystems:

Teradata

Strong understanding of the Teradata architecture, utilities (BTEQ,

TPT, FastLoad/MultiLoad), and advanced SQL/Stored Procedure development.

Hadoop

Experience with the Hadoop ecosystem (HDFS, YARN, Hive) and hands-on proficiency with PySpark for large-scale data processing.

Enterprise

ETL

Demonstrable experience designing and building complex workflows in an enterprise ETL tool like Informatica PowerCenter.

Google Cloud Platform (Google Cloud Platform)

Proficiency

Demonstrable hands-on experience designing, building, and operating solutions with a comprehensive set of Google Cloud Platform data services, including:

Core Data Processing & Warehousing

Google BigQuery (including data modeling, performance tuning, cost management), Google Cloud Storage

(GCS), Cloud Dataflow, and Cloud Dataproc

Orchestration & Event-Driven Architecture

Cloud Composer (Managed

Airflow) for complex workflow orchestration, and Pub/Sub and Cloud Functions for building streaming and event-driven data pipelines.

Data Governance & Management

Practical experience using Dataplex for unified data management, security, and governance across data lakes and warehouses.

Core Engineering & Migration Skills

Expert-level proficiency in SQL, including complex joins, window functions, and

performance tuning across different database engines.

Strong programming skills in Python, applying software engineering best practices.

Hands-on experience with Google's migration assessment tools (e.g.,

BigQuery Migration Service, Database Migration Service) to analyze on-premise workloads and accelerate migration.

Deep understanding of data warehousing concepts, ETL/ELT patterns, data modeling, and database design.

Preferred Qualifications (Nice-to-Haves)

Proven Migration Experience

Direct, hands-on experience successfully completing at

least one large-scale on-premise (Teradata, Hadoop, etc.) to Google Cloud Platform.

Certifications

A Google Cloud Professional Data Engineer certification is a plus.