Sr. Data Engineer – Data & Intelligence

Sr. Data Engineer – Data & Intelligence (RDMP)

Location : Frisco, TX ( onsite)

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

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

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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.