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Developer III - Software EngineeringMerican • United States
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Developer III - Software Engineering

Merican
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    United States
  • US
    United States
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Developer III - Software Engineering
The Data Engineer is a hands-on builder who executes against decomposed deliverables across the same end-to-end vertical owned by Senior Data Engineers on the team from ingestion, through modeling and transformation, into the Microsoft Fabric semantic layer and Power BI visualizations. The Data Engineer takes solution designs that Senior Data Engineers have shaped with the business and turns them into working, tested, documented, production-grade code. Light interaction with HR business stakeholders is expected and will grow over time as the engineer matures into the Senior role, but business decomposition and solution design primarily sit with the Senior Data Engineers. The Data Engineer must be located in the Citizen Senior Data Engineers on the HR Analytics team sit with the business, identify authoritative sources, and decompose business objectives into technical designs spanning ingestion, data contracts, modeling, storage, semantic layer, and visualization. The Data Engineer executes against those decomposed designs across the full vertical Lands data from authoritative sources into the team's unified ingestion framework using the patterns specified in the design. Implements the data contracts, models, and transformations sometimes independently and sometimes as specified by the Senior Data Engineer. Builds pipelines in Snowflake (including Iceberg tables) and/or Databricks (Delta Lake) per the storage pattern in the design. Implements testing, data quality checks, and reconciliation. Builds the Fabric semantic layer assets and Power BI visualizations called for in the design. Operates and maintains the data products after delivery. Over time, the Data Engineer grows into more direct business engagement participating in stakeholder meetings, contributing to solution decomposition, and eventually leading conversations independently as preparation for promotion to Senior Data Engineer. The HR Analytics engineering team operates as a single team across the US and India, with clear layers and a shared way of working: The HR Analytics engineering team is structured in three layers that work together an Information Architect who owns end-to-end architecture and engineering standards Principal Data Engineers who set the engineering bar and lead the data engineering team; and Senior Data Engineers, Data Engineers, and Associate Data Engineers who deliver data products across the full vertical. The team operates across the US (Frisco, TX) and India (Hyderabad) and US locations in PST Timezone, with engineers in all regions partnering across the timezone gap to keep delivery moving and to share ownership of data products end-to-end. Business engagement happens through the team's senior engineering and architecture leadership, with broader engineering team participation that grows over time as engineers build domain context and earn trust with stakeholders. Work flows from a business need into a technical solution design, then into a build that spans ingestion through the unified framework, data modeling, transformation across Snowflake and Databricks, semantic layer in Microsoft Fabric, and visualization in Power BI — with quality, testing, documentation, and reliability owned across the whole vertical. The team operates a DevOps model — engineers own their data products in production, share an on-call schedule, and rotate the operations role across the team. This role is a hands-on engineer who builds data products end-to-end alongside senior engineers on the team, with the opportunity to grow into more independent ownership over time. Execute against decomposed solution designs handed off by Senior Data Engineers, delivering production-grade code across the full vertical from ingestion through visualization. Land source data into the team's unified ingestion framework using the appropriate ingestion pattern (batch, CDC, streaming, API) as specified in the design. Implement physical data models and transformation logic in Snowflake (including Iceberg tables) and Databricks (Delta Lake, Unity Catalog) per the design. Apply medallion (bronze, silver, gold) architecture and the team's engineering standards to all data product builds, including naming conventions, documentation, and code review practices. Build assigned components of the semantic layer in Microsoft Fabric (Fabric IQ, OneLake) so business consumers interact with certified, business-meaningful models. Build assigned Power BI visualizations and reports following the design specifications and the team's BI standards. Implement comprehensive testing unit tests, integration tests, data quality checks, reconciliation logic, and SLA-driven alerting. Own pipeline KTLO (Keep the Lights On) for assigned data products, including monitoring, incident response, and ongoing reliability improvements. Write and maintain documentation including source-to-target mappings, data lineage, data dictionaries, and runbooks. Contribute to and uphold the team's DevOps practices Git, CI/CD, automated testing, and code review. Participate in HR business stakeholder meetings to build domain context, ask clarifying questions on assigned work, and grow into solution decomposition over time. Participate in design reviews led by Senior Data Engineers, contributing implementation perspective and growing toward leading designs independently. Collaborate with technical teams and share knowledge through demos and training sessions. Bachelor's degree in Computer Science, Software Engineering, Information Management, or equivalent experience in field — plus 4+ years of related work experience. Must be located in the Citizen 4+ years of hands-on data engineering experience delivering production data pipelines in enterprise environments. Strong proficiency in SQL and Python, including PySpark and Spark SQL for distributed data transformation. Hands-on experience with Databricks including Delta Lake, Unity Catalog, and workflow orchestration. Hands-on experience with Snowflake at production scale. Working experience with Microsoft Fabric including OneLake; familiarity with Fabric IQ semantic layer concepts. Working experience building data visualizations and reports in Power BI. Experience implementing data ingestion pipelines using batch, CDC, API, or streaming patterns within a unified ingestion framework. Solid data modeling skills, including dimensional modeling and lakehouse modeling patterns at the physical implementation level. Experience implementing pipeline testing unit tests, integration tests, data quality checks, and reconciliation. Experience with DevOps practices for data pipelines Git, CI/CD, and automated testing. Good communication skills, with the ability to convey technical progress and ask clarifying questions of both technical leads and business stakeholders. Strong problem-solving skills and the ability to execute independently on well-defined technical work in a fast-paced, agile environment. Experience with Iceberg tables or other modern open table formats. Experience with HR data domains — talent acquisition, workforce analytics, compensation, learning, performance, or people analytics. Familiarity with Workday, ServiceNow HR, or comparable HR systems of record as authoritative sources for analytics. Exposure to real-time streaming technologies including Kafka, Azure Event Hub, Delta Live Tables, or Spark Structured Streaming. Exposure to AI/ML pipelines or building data products that support ML workloads. Familiarity with legacy data platforms such as Teradata, Oracle, or SQL Server. Azure certifications or demonstrated experience with Azure-native data platform services. Familiarity with Client Omni lakehouse platform, MagentaBuilt integrations, or enterprise IT architecture standards. Familiarity with data privacy and regulatory compliance for HR data (GDPR, CCPA, employee data protection). TekWissen® Group is an equal opportunity employer supporting workforce diversity.
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