Senior Data Engineer (GCP)

Senior Data Engineer (GCP) 6 mo C2H 100% remote Industry - digital marketing

The Senior Data Engineer is a technologist passionate about data in all forms - whether stored within a relational database, a data warehouse, a data lake, a Lakehouse or in-transit in ETL pipelines. They independently produce capable data structures and performant queries.

As a Senior Data Engineer, you will architect and implement data solutions from scratch to extract and land data from various sources that will deliver insights, visualizations, or better predictions for our clients. You will support our software development teams, data analysts, and data scientists using market-relevant products and services.

Responsibilities/Duties

Using complex SQL knowledge and experience, will work with relational databases, Big Query, query authoring (SQL), and working familiarity with various databases. Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other critical business performance metrics. Design and implement scalable and reliable data pipelines on GCP. Implement Change Data Capture (CDC) techniques and manage Delta Live Tables for real-time data integration and analytics, ensuring data consistency and enabling incremental data updates in cloud-based data platforms. Design, configure, and manage Data Lakes in GCP, utilizing services like Google Cloud Storage, BigQuery, and Dataproc, to support diverse data types and formats for scalable storage, processing, and analytics. API architecture design, including RESTful services and microservices, integrating Machine Learning models into production systems to enhance data-driven applications and services. Build the infrastructure, using IaC, required for extraction, transformation, and loading (ETL) of data from a wide variety of data sources using SQL and GCP Migrate and create data pipelines and infrastructure from AWS or Azure to GCP. Write and maintain robust, efficient, scalable Python scripts for data processing and automation. Use a strong understanding of data pipeline design patterns, and determine the best for the use case. Work with unstructured datasets. Build processes supporting data transformation, data structures, metadata, dependency, and workload management. Manipulate, process, and extract value from large, disconnected datasets. Work with stakeholders, including the Executive, Product, Data, and Design teams, to assist with data-related technical issues and support their data infrastructure needs. Assume responsibility for the stability of the data in transit and at rest Collaborate directly with the client to identify and implement data security and compliance requirements. Keep client data secure using best practices. Foster cross-functional collaboration as a technical liaison between engineering and other project disciplines (Design, Quality, Project Management, Strategy, Product, etc.)

Skills and Qualifications

Bachelors in Computer Science or related field or equivalent experience required 6+ years of relevant experience In-depth knowledge of Google Cloud Platform (GCP) data services such as BigQuery, Dataflow, Dataproc, and Pub/Sub, with proven experience in designing and implementing data pipelines, data storage, and analytics solutions in GCP. Ability to take technical requirements and produce functional code Experience with Git and specified technologies. Proficiency in Python and SQL. Experience with migrating data pipelines and infrastructure to GCP from multiple infrastructure stacks. Deep understanding of data modeling, ETL processes, and data warehousing principles. Familiarity with data pipeline orchestration tools and practices, such as Pub/Sub, Streaming, and Cloud Functions. Excellent problem-solving and analytical skills. Ability to communicate with technical and non-technical client stakeholders Proactive collaborator works with colleagues to improve their technical aptitude Experience using and/or creating APIs Experience with any of the following additional database management systems: MS SQL Server, MongoDB, PostgreSQL, NoSQL (e.g. Cassandra), Sybase, IBM Db2, or Oracle Database