Data Engineer

Data EngineerThe Data Engineer will serve as the Office of Finance's subject matter expert on a multitude of data engineering methods, data integration and data management technologies. This is a highly technical role responsible for leading the data engineering lifecycle across the organization's data planes — from raw data ingestion through data cleansing, data standardization, data transformation, data modeling, and data delivery. The scope of the role spans data integration with on-premises source systems through cloud-based data processing, data storing, and works in tandem with other teams who support data serving and data delivery layers.The Data Engineer works collaboratively across internal data stakeholders and data consumers to identify, prove and implement opportunities to improve data discovery, data collection, data transformation, data standardization, data storage, and data quality. The Data Engineer assists data stakeholders in maintaining an enterprise view of the organization's data assets, and works with Product Owners/Leaders to consider opportunities to enhance both the organization and the FHLBanks System at large via compelling data products.Our employees are committed to and exemplify our Core Values:Integrity through accountability, consistency, transparency and trustAgility through adaptability, continuous improvement, expertise, and flexibilityPartnership through collaboration, communication, leadership, and teamworkInclusivity through diversity, relationships, respect, and supportPrincipal Responsibilities:Design and implement data ingestion, integration, and transformation solutions that consolidate enterprise data from multiple sources.Develop and implement data pipelines to cleanse, standardize, validate and enrich data to ensure data accuracy, consistency, and fitness for downstream use.Apply data profiling and statistical analysis techniques to characterize data distributions, identify anomalies, detect structural problems, and support overall data quality.Implement and automate data quality controls and monitoring to identify, prevent, and remediate data issues throughout the data lifecycle.Build dimensional models, fact tables, and semantic layers that support downstream analytics and reusability of business data.Assist data stakeholders in documenting data assets including lineage, data dictionaries, and ownership through the enterprise data catalog.Monitor ETL/ELT data pipeline health and data quality metrics through observability and quality tools, taking proactive steps to address data quality issues before they impact downstream consumers.Participate in on-call rotation as needed for support of data products and pipelines.Assist with other job duties as assigned.Principal Requirements:Bachelor's degree in Computer Science, Statistics, Mathematics, Finance, Financial Engineering, Quantitative Finance, Information Science, Data Engineering, or a related quantitative field. Master's degree or above preferred. A combination of advanced education and directly related experience may be combined to demonstrated subject matter expertise, provided education is a graduate or terminal degree.Subject matter expertise in the following areas:At least 5-7 years of data engineering experience with demonstrated ownership of Production data pipelines.At least 5-7 years demonstrated experience in applied exploratory data analysis, descriptive statistical analysis, and inferential statistical analysis in the development and delivery of enterprise data products and data visualizations.At least 3-5 years of hands-on experience with ETL/ELT including job design, dataflow optimization, and integration.At least 3-5 years of experience with industry leading analytical data platforms, (e.g., Azure Data Factory, Synapse, Databricks, Azure Data Lake Storage, Delta Lake, Spark SQL, and Unity Catalog, or other comparable Azure cloud data services.)Prior experience in financial services, capital markets, or government sponsored entities strongly preferred.Technical skills:Programming/Scripting: Python (pandas, PySpark, SQL Alchemy or other similar data engineering scripting tooling), SQL (proficient), Bash (optional)Data Integration: Azure Data Factory, Azure Synapse Pipelines or other comparable toolingStorage: Azure Data Lake Storage or comparable, PostgreSQL (Familiar), SAP ASE (Optional)Analytics Engineering: Azure Synapse Analytics, Delta Live Tables, Apache Spark, or other comparable toolingBI/Reporting: Power BI (proficient), SAP BusinessObjects (optional)DevOps: GitHub Enterprise, CI/CD pipelinesData Governance: Microsoft Purview or comparable, Data lineage, cataloging, access controlObservability: Datadog, Grafana, Prometheus, or comparable toolingAbility to develop and refine an evolving understanding of business requirements and needs.Ability to rapidly iterate upon ideas as on-going mechanism to progressively validate business value and seek clarity in desired business outcomes.Ability to communicate well, both orally and in writing, including producing thorough documentation of all work.Ability to conduct independent technical research and share results with management and/or peers.Ability to listen and integrate ideas from different views, build and maintain respectful relationships, collaborate with others, and resolve conflicts constructively.Proof of eligibility to work in the United States.This position has an annualized salary range of $138,375 - $212,218. The final salary offered within this range is dependent on various factors, including but not limited to the responsibilities of the position, the experience, skill set and other relevant qualifications of the applicant and internal pay equity.Equal Employment Opportunity:The Federal Home Loan Banks Office of Finance is committed to equal employment opportunity without regard to race (including traits historically associated with race, such as hair texture, hair type and protective hairstyles), color, religion, sex, pregnancy (including childbirth, lactation, and related medical conditions), national origin or ancestry, ethnic origin, age, physical or mental disability, veteran status, uniformed service member status, military status, sexual orientation, gender identity, status as a parent, marital status, genetic information (including testing and characteristics), citizenship or immigration status, or any other characteristic protected by applicable federal, state, or local law.