Sr. Data Engineer - Data & Intelligence

TekWissen•Frisco, TX

1d•Onsite

About The Position

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. This role involves architecting, designing, and overseeing the development of enterprise-scale ELT/ETL pipelines for finance and revenue data. The position requires defining and enforcing standards for various data ingestion patterns, ensuring scalable and fault-tolerant pipeline design, and providing technical leadership for complex data integration projects. The Sr. Data Engineer will also lead the architecture and adoption of Snowflake and Databricks platforms, define best practices for these platforms and dbt, and establish orchestration frameworks using Airflow or Azure Data Factory. Additionally, the role involves architecting and optimizing cloud-native data platforms on Azure or AWS, driving cost optimization strategies, and providing deep technical leadership in advanced SQL, Python/PySpark, and streaming/real-time data processing. The engineer will also establish enterprise frameworks for data quality, validation, and observability, support data modeling efforts, define and enforce CI/CD standards, lead the implementation of security and governance controls, and possess a deep understanding of finance and revenue data domains. Strong soft skills for technical leadership and collaboration are essential.

Requirements

Advanced SQL (query tuning, execution optimization, complex transformations)
Python / PySpark for distributed data processing
Spark, Scala (where applicable), and automation scripting
Kafka / Event Hub and Spark Structured Streaming
Stateful processing, watermarking, checkpointing, and fault tolerance patterns
Data quality, validation, and observability frameworks
Automated testing (unit, integration, regression)
Data validation (completeness, accuracy, consistency)
Data quality tools (dbt tests, Great Expectations, custom frameworks)
SLA monitoring, alerting, and data freshness tracking
Enterprise data models (star, snowflake, data vault)
SCD (Type 1/2) strategies
Partitioning, clustering, and performance optimization
CI/CD standards for data engineering (GitHub Actions, Azure DevOps)
Code quality, versioning, and deployment best practices (branching strategies, PR reviews, release pipelines)
Environment promotion (dev → QA → prod) and release management
Reusable frameworks and templates
Enterprise-grade security and governance controls: RBAC, row/column-level security
PII and CPNI compliance (TISS-310)
Secrets management and secure pipeline design
Data lineage, auditability, and compliance readiness
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
Technical leadership and escalation point across engineering teams
Collaboration with architects, product managers, analysts, and business stakeholders
Clear communication of complex technical topics to technical and non-technical audiences
Leading incident reviews and ensuring continuous improvement

Nice To Haves

Cloud-native data platforms on Azure (ADLS Gen2, Event Hub, ADF, Key Vault) or AWS equivalents
Infrastructure-as-code (Terraform, Bicep)
Cost optimization strategies (compute sizing, storage design, partitioning, workload isolation)
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)
Orchestration frameworks using Airflow / Azure Data Factory

Responsibilities

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.
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), and 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.
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.
Provide deep technical leadership in Advanced SQL (query tuning, execution optimization, complex transformations) and 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.
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.
Establish enterprise frameworks for data quality, validation, and observability.
Define standards for Automated testing (unit, integration, regression), Data validation (completeness, accuracy, consistency), and 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.
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.
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.
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.
Guide engineering teams on accurate implementation of finance logic.
Ensure high data integrity standards for regulated financial data.
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.