Data Engineer Data Intelligence

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 MODELING 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.