Sr. Data Engineer – Data & Intelligence with FAANG or product-based company Experience

Hello,

Hope you are doing well,

FAANG or product-based company or large-scale data platform

Location - Frisco TX

Must have
ETL/ELT pipeline development
Snowflake
Databricks
dbt
Azure

Sr. Data Engineer – Data & Intelligence
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Summary
Senior Data Engineer / Technical Lead who can architect and lead enterprise finance-data pipelines using Snowflake + Databricks + Python/PySpark + SQL, with strong Azure/cloud, streaming, CI/CD, data quality, and finance/revenue data experience.

Additional skills:
Snowflake + Databricks + Python/PySpark + SQL, with strong Azure/cloud, CI/CD


Comments
Looking for -
Senior/Lead Data Engineer with Snowflake + Databricks + PySpark + Azure, who has built high-volume enterprise data pipelines and preferably worked with telecom/finance/revenue/billing data

Years of exp required - 8-10 years
Location - Frisco, TX

Must have
Must-have skills: Snowflake, Databricks, PySpark, Python, advanced SQL, Azure, and CI/CD.
Nice to have
Exp with telecom/finance/revenue/billing data