Data Engineer

FEDERAL HOME LOAN BANKS OFFICE OF FINANCE•Reston, VA

1d•$138,375 - $212,218

About The Position

The 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. We’re proud of the way our teammates have a positive impact on everything we do. Our employees are committed to and exemplify our Core Values: Integritythrough accountability, consistency,transparencyand trust Agilitythrough adaptability, continuous improvement,expertise, and flexibility Partnershipthrough collaboration, communication, leadership, and teamwork Inclusivitythrough diversity, relationships, respect, and support

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.
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.
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 tooling
Storage: Azure Data Lake Storage or comparable, PostgreSQL (Familiar), SAP ASE (Optional)
Analytics Engineering: Azure Synapse Analytics, Delta Live Tables, Apache Spark, or other comparable tooling
BI/Reporting: Power BI (proficient), SAP BusinessObjects (optional)
DevOps: GitHub Enterprise, CI/CD pipelines
Data Governance: Microsoft Purview or comparable, Data lineage, cataloging, access control
Observability: Datadog, Grafana, Prometheus, or comparable tooling
Ability 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.

Nice To Haves

Master’s degree or above preferred.
Bash (optional)
SAP ASE (Optional)
SAP BusinessObjects (optional)

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.