Sr Data Engineer || Dallas, TX - 2-3 days Hybrid || 12+ months || Video

Title: Sr Data Engineer (Contract)

Location: Dallas, TX 75207 - Hybrid - 2-3 days

Duration: 12 Month Contract to Hire

• 3 month initial for POC
• 9 month implementation phase

MOI: Video

Proper LinkedIn

Start Date: End of September/1 st week of October

Please get expectation for conversion Salary

HOW YOU EXPLAIN THIS PROJECT:

Client is seeking a Senior Data Engineer to join a high-profile data transformation initiative for a rapidly growing medical device organization. This is a unique opportunity to become a key contributor on a greenfield Azure Data Lake implementation , helping establish the foundation for the company's long-term enterprise data strategy.

The selected consultant will join during Phase 2 of an active Proof of Concept (POC) and work directly alongside Judge Consulting architects, data engineers, business stakeholders, and client leadership to evolve the platform into a full-scale enterprise solution. Successful performance during the engagement is expected to lead into continued participation throughout the broader implementation, with a strong likelihood of conversion to a permanent role with the medical device company upon project completion.

• Microsoft Fabric, Azure Data Platform & Modern Data Engineering

Position Summary

Judge Consulting is seeking a hands-on Data Engineer to design, build, optimize, and support modern data solutions across Microsoft Fabric and Azure. The ideal candidate combines strong software engineering discipline with practical experience delivering reliable ingestion, transformation, modeling, orchestration, and data-quality capabilities. This role requires an engineer who can work independently, collaborate with technical and business stakeholders, and translate data requirements into secure, scalable, maintainable solutions.

Core Responsibilities

• Data ingestion and integration. Build batch and incremental pipelines from APIs, files, databases, and cloud services using reusable patterns with logging, retry, monitoring, and error handling.
• Lakehouse engineering. Develop bronze, silver, and gold data layers using Delta Lake and medallion architecture principles, including schema management, data conformance, and performance optimization.
• Data transformation. Create production-grade PySpark and SQL transformations that handle complex joins, business rules, deduplication, normalization, and historical processing.
• Data modeling. Design dimensional and analytical models that are understandable, performant, and aligned to reporting and analytics requirements.
• Orchestration and operations. Implement scheduled and event-driven workflows, dependencies, alerting, telemetry, restartability, and operational support procedures.
• Data quality and reconciliation. Establish automated validation, control totals, exception handling, lineage, and reconciliation processes to confirm completeness and accuracy across data layers.
• Engineering practices. Use Git, branching, pull requests, CI/CD, environment promotion, configuration management, documentation, and peer review to deliver maintainable data products.
• Stakeholder collaboration. Partner with architects, analysts, application teams, security, and business stakeholders to refine requirements, resolve data issues, and communicate technical decisions.
• Metric definition and validation. Partner with business and finance stakeholders to define metrics, map source data, establish validation criteria, and reconcile results against existing reports.

Required Technical Skills

• Microsoft Fabric. Hands-on experience with OneLake, Lakehouse, Notebooks, Data Factory pipelines, SQL endpoints, workspaces, and deployment practices,
• Microsoft Fabric capacity management. Experience monitoring Microsoft Fabric capacity, workload usage, throttling, performance, and cost.
• PySpark and Delta Lake. Strong production experience with Spark dataframes, Delta MERGE, schema evolution, partitioning, time travel, OPTIMIZE, VACUUM, and performance troubleshooting.
• Advanced SQL. Expertise in complex querying, window functions, stored logic, data profiling, query tuning, and dimensional modeling.
• Pipeline design. Experience building reliable batch, incremental, CDC, and idempotent pipelines that can run unattended and recover cleanly from failure.
• Azure data services. Working knowledge of Azure Data Factory, Synapse Analytics, ADLS Gen2, Key Vault, Entra ID, managed identities, and private connectivity patterns.
• DevOps for data. Experience with source control, automated testing, CI/CD pipelines, parameterized deployments, secrets management, and separation of development, test, and production environments.
• Security and governance. Understanding of least privilege, role-based access, encryption, auditability, data classification, lineage, retention, and secure handling of sensitive or regulated data.
• Tableau development. Hands-on experience connecting Tableau Cloud to curated data sources and developing dashboards, reports, calculations, filters, and row-level security

Professional Qualifications

• Experience. Demonstrated success delivering production data platforms or enterprise analytics solutions in cloud environments.
• Problem solving. Ability to profile unfamiliar datasets, identify source-system issues, troubleshoot pipeline failures, and recommend practical remediation options.
• Communication. Strong written and verbal communication skills, including clear technical documentation and effective participation in business-facing working sessions.
• Delivery mindset. Self-directed, accountable, detail-oriented, and comfortable balancing quality, maintainability, security, and delivery priorities.

Preferred Experience

• Domain data. Experience with healthcare, financial, operational, or other regulated datasets, including reconciliation against authoritative reports or source systems.
• Healthcare interoperability. Familiarity with medical billing and revenue-cycle data such as 837 claims, 835 remittance, CARC/RARC codes, claim-to-remittance matching, partial payments, and service-line adjudication.
• Analytics consumption. Experience preparing curated datasets and dimensional models for Tableau or other downstream analytics workloads.
• Platform transition. Experience modernizing workloads from Synapse, ADF, ADLS Gen2, SQL Server, or other cloud data platforms into Microsoft Fabric.

Ways of Working

• Secure delivery. Work within client-owned repositories, subscriptions, access controls, and approved development tooling while protecting confidential and regulated data.
• Documentation and handoff. Maintain architecture notes, mappings, runbooks, support procedures, and knowledge-transfer materials as part of normal engineering delivery.
• Quality ownership. Validate data and pipeline behavior before release, surface dependencies and risks early, and provide evidence that solutions meet agreed technical and business requirements.

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