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: Lead I - Software Engineering
Location: Frisco TX
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 Modelling 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.
Principal - Level Expectations:
- Own and drive enterprise-level data engineering strategy and execution.
- Lead delivery of large, complex, multi-domain data platforms.
- Mentor senior engineers and define technical direction for the team.
- Drive tooling, architecture, and platform decisions across programs.
- Identify and lead technical debt reduction and modernization initiatives.
- Establish best practices, reusable components, and platform standards at scale.
- Influence cross-functional teams and leadership decisions on data platform strategy.
TekWissen Group is an equal opportunity employer supporting workforce diversity.
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