Data Engineer III

Position Summary:

Looking to hire a Data Engineer at the G4 level who has a deep understanding of Finance Data Product Lifecycle, Standards and Practices. Will be responsible for building scalable and efficient data solutions to support Finance Analytics and reporting with a specific focus on finance data products and initiatives (e.g., P&L, revenue, cost, margin, and forecasting data). As a Data Engineer, you will collaborate with data scientists, analysts, and other cross- functional teams to ensure the availability, reliability, and performance of finance data systems. Lead initiatives to enable trusted financial data, supports decision-making, and partners with Finance, and Technology teams to deliver scalable data solutions that drive insights into financial performance, variance drivers, profitability, and forecast accuracy. Expertise in cloud computing platforms, technologies and data engineering best practices will play a crucial role within this domain

Primary Responsibilities:

• Builds and maintains relevant and reliable finance data products that support financial reporting, FP&A, and decision analytics. Develops and implements new technology solutions needed to ensure ongoing improvement with data reliability and observability in- view.
• Participates in new software development engineering and leads data engineering initiatives supporting planning, forecasting, and variance analytics, ensuring timely and accurate delivery of finance analytics and datasets.
• Design, build, and operate batch and streaming pipelines on GCP using services such as Cloud Storage (GCS), Pub/Sub, Dataflow, Dataproc, and Cloud Composer (Airflow) to deliver curated finance datasets and analytics-ready data products.
• Work closely with Finance product owners and business partners to define business rules that determine the quality of financial datasets (e.g., chart of accounts mappings, cost center hierarchies, fiscal calendar, allocations).
• Drive and implement best practices for pipeline development, data governance, data security, and quality across finance datasets; ensure controls and auditability aligned to internal policies.
• Ensure scalability, maintainability, and quality of data systems powering revenue and expense reporting, close and consolidation inputs, and management reporting analytics.
• Staying up to date with emerging data engineering technologies, trends, and best practices, and evaluating their applicability to meet evolving Finance Analytics needs.
• Documenting data engineering processes, workflows, data lineage, and solutions for knowledge sharing and future reference.
• Mentor and coach junior data engineers, particularly in areas related to financial data modeling, governance, and analytics enablement.
• Ability and flexibility to coordinate and work with teams distributed across time zones, as needed.

Qualifications

• Lead teams to drive scalable data engineering practices and technical excellence within the Finance Data ecosystem.
• Bachelor's or master's degree in computer science or related engineering field and deep experience with cloud computing on GCP (e.g., BigQuery, Cloud Storage/GCS, Pub/Sub, Dataflow, Dataproc, Cloud Composer) and core platform services (IAM, Secret Manager, Cloud KMS, Logging/Monitoring)
• 5+ years of professional experience in data engineering or related fields
• Proficiency in Python, Java, or Scala for data processing and automation
• Hands-on experience with data orchestration tools (e.g., Apache Airflow / Cloud Composer, Luigi) and distributed processing frameworks (e.g., Spark on Dataproc, Dataflow) data ecosystems and platforms (e.g., BigQuery, Hadoop, Spark, NoSQL, Kafka/Pub/Sub)
• Expert knowledge of data quality functions like cleansing, standardization, parsing, de- duplication, mapping, hierarchy management, etc., ideally applied to finance master and reference data (e.g., chart of accounts, cost centers).
• Ability to perform extensive data analysis (comparing multiple datasets) using a variety of tools
• Proven ability to mentor team members and lead technical initiatives across multiple workstreams
• Effective communication and stakeholder management skills to drive alignment and adoption of data engineering standards
• Demonstrated experience in data management & data governance capabilities
• Familiarity with data warehousing principles and best practices (e.g., dimensional modeling, BigQuery partitioning/clustering, cost/performance optimization).
• Excellent problem solver - use of data and technology to solve problems or answer complex data related questions
• Excellent collaboration skills to work effectively in cross-functional teams.