Sr. Data Engineer
Job
Halvik
Vienna, VA (In Person)
Full-Time
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Job Description
Halvik Corp delivers a wide range of services to 13 executive agencies and 15 independent agencies. Halvik is a highly successful WOB business with more than 50 prime contracts and 500+ professionals delivering Digital Services, Advanced Analytics, Artificial Intelligence/Machine Learning, Cyber Security and Cutting-Edge Technology across the US Government. Be a part of something special! Role Summary The Senior Data Engineer will be a key contributor within the Data Products Delivery Area, responsible for designing, building, and operating scalable, secure, and high-quality data pipelines that power Business Intelligence & Analytics and AIML products. This role supports a Databricks Data Lakehouse using Medallion Architecture, built on Data Fabric principles, and operates within a release-based Agile framework. This role requires strong hands-on development using Python, PySpark, SQL, and APIs, and experience integrating diverse enterprise and external data sources. Key Responsibilities Data Engineering & Architecture
- Design, develop, and optimize end-to-end data pipelines using Python and PySpark.
- Build and maintain data ingestion frameworks leveraging REST APIs, streaming APIs, and batch interfaces.
- Implement Medallion Architecture (Bronze, Silver, Gold) within a Databricks Lakehouse.
- Integrate data from structured, semi-structured, and unstructured sources, including API-based and event-driven sources.
- Apply Data Fabric principles including metadata-driven ingestion, lineage, observability, and reusability.
- Collaborate with Senior Data Architects on logical and physical data models. Data Products & Delivery
- Deliver data assets as releasable products
- Support downstream consumers including: o BI&A dashboards and visualizations o AIML feature engineering, training, and inference pipelines o Data services and curated datasets exposed via APIs
- Decompose requirements into well-defined user stories aligned with MoSCoW prioritization. Data Quality, Governance & Security
- Implement data validation, reconciliation, and quality checks within PySpark pipelines.
- Ensure secure handling of PII and sensitive data, including encryption, masking, and access controls.
- Partner with CloudOps (DevSecOps, FinOps, InfraOps) to ensure: o Secure API access and secrets management o CI/CD automation for Python and PySpark workloads o Cost-optimized compute and storage usage Production Support & Optimization
- Support O&M for production data pipelines, APIs, and analytics products.
- Tune Spark jobs, SQL queries, and API integrations for performance and reliability.
- Implement monitoring, logging, and alerting for data workflows.
- Drive automation and refactoring to improve resiliency and scalability. Collaboration & Leadership
- Mentor junior and mid-level data engineers on Python, PySpark, and API integration best practices.
- Work closely with: o Business Analysts & Data Architects o Data Scientists & ML Engineers o BI Developers o Automation Test Engineers
- Contribute to reusable frameworks, coding standards, and engineering best practices. Required Skills & Experience Core Technical Skills
- Strong hands-on experience with: o Python for data engineering and API integration o PySpark / Apache Spark for large-scale data processing o SQL (advanced querying and performance tuning)
- Proven experience building API-driven data ingestion pipelines (REST, JSON, OAuth, pagination, throttling).
- Strong experience with Databricks, Delta Lake, and Lakehouse Architecture.
- Experience implementing Medallion Architecture in production environments. Cloud & DevOps
- Experience working in cloud platforms (AWS, Azure, or GCP).
- Familiarity with: o CI/CD pipelines for Python and Spark workloads o Infrastructure as Code concepts o Secure API authentication and secrets management
- Cost-conscious engineering mindset (FinOps exposure is a plus). Data Management & Analytics
- Experience supporting BI&A workloads at scale.
- Understanding of data modeling for analytics and reporting.
- Experience enabling data consumption for AI/ML use cases. Agile & Delivery
- Proven experience working in Agile Scrum / Scrum of Scrums environments.
- Comfortable working with release-based delivery and production support. Nice to Have
- Experience exposing data via internal or external APIs.
- Familiarity with streaming technologies (Kinesis, Firehose, Kafka, Event Hubs, etc.).
- Experience with data governance, cataloging, and lineage tools.
- Exposure to feature stores or ML data pipelines. Soft Skills
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Ownership mindset with a focus on reliability and quality.
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