Data Engineer

This role requires candidates who are currently authorized to work in the U.S. without sponsorship, and C2C arrangements are not accepted. This role is onsite near Tustin, CA. Overview We are seeking a Data Engineer to support enterprise data transformation and modernization initiatives within a growing Data Engineering organization. This role will focus on designing, building, and maintaining reliable data pipelines, transforming business-critical datasets, and supporting the creation of reusable data products across the enterprise. This is not a narrow ETL role where data is simply moved from one system to another. The ideal candidate understands the business purpose behind the data, why the pipeline matters, how the data will be consumed, and what outcome the work is supporting. This person should be comfortable working closely with product owners, BSAs, analytics teams, BI teams, business stakeholders, and platform engineers to deliver clean, well-documented, production-ready data solutions. The right candidate will bring technical strength, curiosity, documentation discipline, and a product mindset to a fast-moving data modernization environment. Key Responsibilities Design, build, test, and maintain scalable ETL/ELT data pipelines Transform, model, and optimize data from enterprise, operational, transactional, and reporting sources Support modernization of data warehouses, data lakes, data marts, analytics platforms, and cloud-based data environments Build reliable data solutions using tools such as Databricks, Spark, Azure Data Factory, ADLS, Snowflake, SQL Server, DBT, Airflow, or similar platforms Use SQL, Python, PySpark, Scala, Java, or similar technologies to develop data pipelines and data transformation logic Partner with BSAs, Product Owners, Data Platform Engineers, BI, Analytics, Architecture, and business stakeholders Understand the business reason behind each data pipeline, dataset, report, or data product being built Work within product-oriented data pods aligned to business areas such as finance, marketing, capital markets, servicing, customer operations, or enterprise reporting Translate requirements into technical designs, pipeline logic, data models, and reusable engineering patterns Validate data accuracy, completeness, quality, lineage, and usability across source and target systems Identify gaps in requirements, source data, definitions, transformations, or downstream reporting needs Support performance tuning, troubleshooting, automation, testing, deployment, and production support Participate in proof-of-concept work, prototyping, release planning, delivery estimation, and platform modernization efforts Use Git-based workflows, including branching, merging, pull requests, code reviews, and reusable code practices Document pipeline designs, data flows, business logic, assumptions, dependencies, gaps, and decisions clearly Communicate blockers, risks, dependencies, and technical tradeoffs to product, project, and engineering leadership Required Qualifications 3+ years of experience in Data Engineering, Analytics Engineering, Backend Engineering, Software Engineering, or enterprise data platform development Strong SQL skills, including querying, transformations, joins, performance tuning, data modeling, and troubleshooting Hands‑on experience designing, building, or maintaining ETL/ELT data pipelines Experience with cloud data platforms, data warehouses, data lakes, relational databases, or modern analytics platforms Experience with at least one data engineering language such as Python, PySpark, Scala, Java, or similar Experience working with structured, semi-structured, and enterprise data sources Familiarity with APIs and common data formats such as REST, GraphQL, XML, JSON, CSV, parquet, or similar Working knowledge of Git and modern software development practices Ability to work closely with business, analytics, product, and engineering teams Strong problem‑solving skills and ability to operate in ambiguity Strong communication and documentation skills Ability to understand why data matters to the business, not just how to move it Preferred Qualifications Experience with Databricks, Spark, Azure Data Factory, ADLS, Snowflake, SQL Server, DBT, Airflow, or similar tools Experience working on data modernization, data migration, data warehouse modernization, or analytics transformation initiatives Experience supporting data products, business-facing datasets, reporting layers, dashboards, or analytics use cases Experience with finance, marketing, mortgage, banking, servicing, capital markets, customer, or operational datasets Experience with CI/CD, automated testing, reusable code patterns, and production deployment practices Experience with Power BI, Tableau, SSRS, SSAS, or enterprise reporting environments Understanding of data quality, data governance, lineage, access controls, and source‑of‑truth reporting Exposure to Azure, AWS, or GCP cloud ecosystems Experience working in product pods or cross‑functional engineering teams What This Role Solves Legacy pipelines and data environments that need to be modernized Data movement without enough business context or product thinking Gaps between business requirements and technical implementation Inconsistent data quality, unclear lineage, or incomplete documentation Business teams needing reliable, reusable, and trusted datasets Engineering workstreams that need stronger execution, validation, and documentation What Success Looks Like Data pipelines are reliable, scalable, tested, and well‑documented Business users and analytics teams receive clean, trusted, usable datasets Engineering work is aligned to real business outcomes Data products are easier to understand, maintain, and reuse Requirements gaps and data issues are identified early Pipelines support modernization of the broader enterprise data platform The engineer can clearly explain what was built, why it matters, and how it supports the business Ideal Candidate Profile The ideal candidate is a product-oriented Data Engineer who understands that data engineering is not just about moving data from point A to point B. They care about the business purpose of the data, the quality of the output, and how the data will be used by downstream teams. This person is curious, hands‑on, and comfortable working through ambiguity. They can partner with product owners and BSAs to clarify requirements, work independently inside a pod, and document their work so others can build on it. Successful candidates will bring strong technical execution, clear communication, and a bias toward action. They should be comfortable in a transformation environment where teams are building new data products, improving legacy processes, and creating a more scalable data platform. #J-18808-Ljbffr Veritas Search Group