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. Our client is a provider of digital technology and transformation, information technology and services. This role is for a Sr. Data Engineer focusing on Data & Intelligence, involving the architecture, design, and development of enterprise-scale data pipelines and platforms. The position requires deep technical leadership in data engineering practices, cloud infrastructure, streaming technologies, data quality, modeling, DevOps, security, and governance, with a strong emphasis on finance and revenue data domains.

Requirements

Advanced SQL (query tuning, execution optimization, complex transformations)
Python / PySpark for distributed data processing
Experience with Snowflake (Snowpipe, streams, tasks, query optimization, cost efficiency)
Experience with Databricks (PySpark, Delta Live Tables, Unity Catalog, job optimization)
Experience with dbt (modular design, testing frameworks, CI/CD integration, reusable components)
Experience with orchestration frameworks like Airflow / Azure Data Factory
Experience with cloud-native data platforms on Azure (ADLS Gen2, Event Hub, ADF, Key Vault) or AWS equivalents
Experience with infrastructure-as-code (Terraform, Bicep)
Experience with real-time and near real-time data processing solutions using Kafka / Event Hub and Spark Structured Streaming
Experience with data quality, validation, and observability frameworks
Experience with automated testing (unit, integration, regression)
Experience with data validation (completeness, accuracy, consistency)
Experience with data quality tools (dbt tests, Great Expectations, custom frameworks)
Experience interpreting and implementing enterprise data models (star, snowflake, data vault)
Experience with SCD (Type 1/2) strategies, partitioning, clustering, and performance optimization
Experience with CI/CD standards for data engineering (GitHub Actions, Azure DevOps)
Experience with code quality, versioning, and deployment best practices
Experience with environment promotion (dev → QA → prod) and release management
Experience with enterprise-grade security and governance controls (RBAC, row/column-level security)
Experience with PII and CPNI compliance (TISS-310)
Experience with secrets management and secure pipeline design
Deep understanding of finance and revenue data domains, including billing and revenue systems, GL structures and financial reporting, revenue recognition and reconciliation, and period-end close cycles.
Ability to act as a technical leader and escalation point.
Ability to partner with architects, product managers, analysts, and business stakeholders.
Ability to drive cross-team alignment and solution consistency.
Ability to communicate complex technical topics clearly to both technical and non-technical audiences.
Ability to lead incident reviews and ensure continuous improvement.

Nice To Haves

Scala (where applicable) for automation scripting
Support semantic layer enablement for analytics and reporting.

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) and Databricks (PySpark, Delta Live Tables, Unity Catalog, job optimization).
Define best practices for 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.
Provide deep understanding of finance and revenue data domains, including billing and revenue systems, GL structures and financial reporting, revenue recognition and reconciliation, and period-end close cycles.
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.