Lead I - Software Engineering

Lead I - Software Engineering 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) 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) 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) 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. 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. 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. 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.