Data Engineer

Data Engineer

Role Summary

The Senior Data Engineer is a hands-on expert and technical leader, actively engaged in designing, building, and optimizing scalable, reliable data pipelines at an enterprise level. This role not only guides architectural decisions but also directly implements advanced ELT solutions, troubleshoots complex data challenges, and ensures best practices through practical, high-impact contributions.

This role combines deep hands‑on expertise with technical ownership, mentoring, and architectural alignment. The Senior Data Engineer drives and implements data engineering best practices, ensures high standards for quality and security, and partners with architecture and platform teams to improve the overall data ecosystem.

Key Responsibilities

• Build end-to-end data pipelines and ETL/ELT solutions to support analytics, reporting, and AI/ML use cases, ensuring solutions are robust and production-ready through practical implementation.
• Apply scalable patterns for batch and incremental processing by developing, testing, and deploying data workflows, focusing on hands-on coding and troubleshooting.
• Review and implement data modeling, transformation logic, and performance strategies, using deep technical expertise to optimize and validate solutions.
• Evaluate, select, and integrate tooling, frameworks, and platform capabilities by actively prototyping and configuring systems to meet project requirements.
• Build up complex, high-volume data pipelines using SQL-centric ETL/ELT patterns.
• Design and implement scalable streaming pipelines to process real-time data, ensuring low latency and reliable delivery for analytics and operational use cases.
• Lead performance tuning efforts across pipelines, warehouses, and workloads.
• Ensure data pipelines are resilient, observable, and production-ready.
• Implement enterprise-grade error handling, restartability, and monitoring.
• Build and maintain scalable, low-latency streaming data pipelines using technologies such as Kafka, Kinesis, or Spark Streaming
• Perform on-the-fly data cleaning, validation, and enrichment before data reaches its final destination.
• Uses strategies such as Indexing and partitioning to fine tune the data warehouse and big data environments to improve the query response time and scalability
• Implement standards for data quality checks, validation, and reconciliation.
• Ensure pipelines meet security, access control, and governance requirements.
• Partner with governance \& DataOps teams on metadata, lineage, and auditability.
• Apply consistent naming conventions, documentation, and coding standards.
• Improve operational monitoring, alerting, and incident response processes.
• Proactively identify reliability, performance, and cost optimization opportunities.
• Support and guide production troubleshooting and root‑cause analysis.
• Investigating data quality incidents and identifying design/coding GAPs
• Participate in design and code reviews to enforce quality and best practices.
• Partner with various infrastructure teams, application teams, and architects to generate process designs and complex transformations to various data elements to provide the Business with insights into their business processes.
• Translate ambiguous requirements into well‑designed technical solutions.
• Work in complex multi-platform environments on multiple project assignments.

Required Skills \& Experience

• Advanced expertise in

SQL

, ELT patterns, and performance tuning.

• Strong experience with

Oracle Exadata

,

Snowflake

or similar cloud/on prem data warehouses.

• Hands‑on experience with

enterprise ETL/ELT platforms

(e.g., Talend, dbt, Informatica).

• Deep understanding of

data warehousing architecture and dimensional modeling

.

• Experience designing and supporting large‑scale, production data pipelines.
• Strong scripting experience (Python, shell).
• Experience with data virtualization tools (e.g., Denodo, Composite, dremio, Starburst).
• Experience with DataOps practices, CI/CD, and observability.
• Typically

5 to 7\+ years

of Data Engineering experience.

• ETL Development and Process Support, may require weekend/off business hours work.

Preferred / Nice‑to‑Have

• Experience supporting AI/ML or advanced analytics pipelines.
• Cloud platform experience (AWS, Azure, or GCP).
• Prior experience influencing enterprise data standards or reference architecture.
• Experience optimizing cost and performance in cloud data warehouses.
• Hands-on experience with Cribl, Apache Kafka, Kafka Connect, Spark Streaming, or Apache Flink