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Lead I - Software Engineering

Tekwissen

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: Bellevue/ Frisco

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:
  • o Snowflake (Snowpipe, streams, tasks, query optimization, cost efficiency)
  • o Databricks (PySpark, Delta Live Tables, Unity Catalog, job optimization)
  • o 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:
  • o Advanced SQL (query tuning, execution optimization, complex transformations)
  • o 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:
  • o Automated testing (unit, integration, regression)
  • o Data validation (completeness, accuracy, consistency)
  • o 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:
  • o SCD (Type 1/2) strategies
  • o 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:
  • o RBAC, row/column-level security
  • o 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:
  • o Billing and revenue systems
  • o GL structures and financial reporting
  • o Revenue recognition and reconciliation
  • o 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.
Vacancy posted 5 days ago
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