Sr. Data Engineer - Data & Intelligence

Overview:

TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services

Position: Sr. Data Engineer Data & Intelligence

Location: Frisco, TX, 75034

Duration: 6 Months

Job Type: Temporary Assignment

Work Type: Onsite

Job Description:

DATA PIPELINE DEVELOPMENT

• Architect, design, and oversee development of enterprise-scale ELT/ETL pipelines for finance and revenue data (billing, revenue, GL, opex).
• Define and enforce standards for batch, incremental, and streaming ingestion patterns (CDC, watermarking, event-driven ingestion).
• Ensure idempotent, fault-tolerant, and highly scalable pipeline design across platforms.
• Establish frameworks for error handling, retry strategies, dead-letter queue patterns, and operational resiliency.
• Provide technical leadership for multi-source, high-volume data integration pipelines.

PLATFORM & TOOLING

• Lead architecture and adoption of Snowflake and Databricks platforms for large-scale data processing and analytics.
• Define best practices for:
• Snowflake (Snowpipe, streams, tasks, query optimization, cost efficiency)
• Databricks (PySpark, Delta Live Tables, Unity Catalog, job optimization)
• dbt (modular design, testing frameworks, CI/CD integration, reusable components)
• Establish and govern orchestration frameworks using Airflow / Azure Data Factory, including DAG standards, dependency design, and monitoring.
• Evaluate and drive tooling strategy and platform standardization across teams.

CLOUD INFRASTRUCTURE

• Architect and optimize cloud-native data platforms on Azure (ADLS Gen2, Event Hub, ADF, Key Vault) or AWS equivalents.
• Define standards for infrastructure-as-code (Terraform, Bicep) and environment provisioning.
• Drive cost optimization strategies (compute sizing, storage design, partitioning, workload isolation).
• Ensure platforms are scalable, secure, and production-ready.

LANGUAGES & FRAMEWORKS

• Provide deep technical leadership in:
• Advanced SQL (query tuning, execution optimization, complex transformations)
• Python / PySpark for distributed data processing
• Guide teams on best practices, reusable frameworks, and performance optimization.
• Oversee development standards for Spark, Scala (where applicable), and automation scripting.

STREAMING & REAL-TIME

• Architect real-time and near real-time data processing solutions using Kafka / Event Hub and Spark Structured Streaming.
• Define patterns for stateful processing, watermarking, checkpointing, and fault tolerance.
• Lead implementation of real-time finance/revenue use cases such as reconciliation, anomaly detection signals, and operational reporting.

DATA QUALITY & TESTING

• Establish enterprise frameworks for data quality, validation, and observability.
• Define standards for:
• Automated testing (unit, integration, regression)
• Data validation (completeness, accuracy, consistency)
• Data quality tools (dbt tests, Great Expectations, custom frameworks)
• Ensure SLA monitoring, alerting, and data freshness tracking across all pipelines.
• Drive proactive data quality and governance practices across teams.

DATA MODELING SUPPORT

• Interpret and implement architect-defined enterprise data models (star, snowflake, data vault).
• Provide guidance on:
• SCD (Type 1/2) strategies
• Partitioning, clustering, and performance optimization
• Collaborate with architects to evolve scalable and reusable data models.
• Support semantic layer enablement for analytics and reporting.

DEVOPS & ENGINEERING PRACTICES

• Define and enforce CI/CD standards for data engineering (GitHub Actions, Azure DevOps).
• Establish code quality, versioning, and deployment best practices (branching strategies, PR reviews, release pipelines).
• Standardize environment promotion (dev QA prod) and release management.
• Drive adoption of engineering excellence practices including reusable frameworks and templates.

SECURITY & GOVERNANCE

• Lead implementation of enterprise-grade security and governance controls:
• RBAC, row/column-level security
• PII and CPNI compliance (TISS-310)
• Define standards for secrets management and secure pipeline design.
• Ensure data lineage, auditability, and compliance readiness across platforms.

FINANCE DOMAIN KNOWLEDGE

• Deep understanding of finance and revenue data domains, including:
• Billing and revenue systems
• GL structures and financial reporting
• Revenue recognition and reconciliation
• Period-end close cycles
• Guide engineering teams on accurate implementation of finance logic.
• Ensure high data integrity standards for regulated financial data.

SOFT SKILLS & COLLABORATION

• Act as a technical leader and escalation point across engineering teams.
• Partner with architects, product managers, analysts, and business stakeholders.
• Drive cross-team alignment and solution consistency.
• Communicate complex technical topics clearly to both technical and non-technical audiences.
• Lead incident reviews and ensure continuous improvement.

TekWissen Group is an equal opportunity employer supporting workforce diversity.