Data EngineerWe 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 ResponsibilitiesDesign, build, test, and maintain scalable ETL/ELT data pipelinesTransform, model, and optimize data from enterprise, operational, transactional, and reporting sourcesSupport modernization of data warehouses, data lakes, data marts, analytics platforms, and cloud-based data environmentsBuild reliable data solutions using tools such as Databricks, Spark, Azure Data Factory, ADLS, Snowflake, SQL Server, DBT, Airflow, or similar platformsUse SQL, Python, PySpark, Scala, Java, or similar technologies to develop data pipelines and data transformation logicPartner with BSAs, Product Owners, Data Platform Engineers, BI, Analytics, Architecture, and business stakeholdersUnderstand the business reason behind each data pipeline, dataset, report, or data product being builtWork within product-oriented data pods aligned to business areas such as finance, marketing, capital markets, servicing, customer operations, or enterprise reportingTranslate requirements into technical designs, pipeline logic, data models, and reusable engineering patternsValidate data accuracy, completeness, quality, lineage, and usability across source and target systemsIdentify gaps in requirements, source data, definitions, transformations, or downstream reporting needsSupport performance tuning, troubleshooting, automation, testing, deployment, and production supportParticipate in proof-of-concept work, prototyping, release planning, delivery estimation, and platform modernization effortsUse Git-based workflows, including branching, merging, pull requests, code reviews, and reusable code practicesDocument pipeline designs, data flows, business logic, assumptions, dependencies, gaps, and decisions clearlyCommunicate blockers, risks, dependencies, and technical tradeoffs to product, project, and engineering leadershipRequired Qualifications3+ years of experience in Data Engineering, Analytics Engineering, Backend Engineering, Software Engineering, or enterprise data platform developmentStrong SQL skills, including querying, transformations, joins, performance tuning, data modeling, and troubleshootingHands-on experience designing, building, or maintaining ETL/ELT data pipelinesExperience with cloud data platforms, data warehouses, data lakes, relational databases, or modern analytics platformsExperience with at least one data engineering language such as Python, PySpark, Scala, Java, or similarExperience working with structured, semi-structured, and enterprise data sourcesFamiliarity with APIs and common data formats such as REST, GraphQL, XML, JSON, CSV, parquet, or similarWorking knowledge of Git and modern software development practicesAbility to work closely with business, analytics, product, and engineering teamsStrong problem-solving skills and ability to operate in ambiguityStrong communication and documentation skillsAbility to understand why data matters to the business, not just how to move itPreferred QualificationsExperience with Databricks, Spark, Azure Data Factory, ADLS, Snowflake, SQL Server, DBT, Airflow, or similar toolsExperience working on data modernization, data migration, data warehouse modernization, or analytics transformation initiativesExperience supporting data products, business-facing datasets, reporting layers, dashboards, or analytics use casesExperience with finance, marketing, mortgage, banking, servicing, capital markets, customer, or operational datasetsExperience with CI/CD, automated testing, reusable code patterns, and production deployment practicesExperience with Power BI, Tableau, SSRS, SSAS, or enterprise reporting environmentsUnderstanding of data quality, data governance, lineage, access controls, and source-of-truth reportingExposure to Azure, AWS, or GCP cloud ecosystemsExperience working in product pods or cross-functional engineering teamsWhat This Role SolvesLegacy pipelines and data environments that need to be modernizedData movement without enough business context or product thinkingGaps between business requirements and technical implementationInconsistent data quality, unclear lineage, or incomplete documentationBusiness teams needing reliable, reusable, and trusted datasetsEngineering workstreams that need stronger execution, validation, and documentationWhat Success Looks LikeData pipelines are reliable, scalable, tested, and well-documentedBusiness users and analytics teams receive clean, trusted, usable datasetsEngineering work is aligned to real business outcomesData products are easier to understand, maintain, and reuseRequirements gaps and data issues are identified earlyPipelines support modernization of the broader enterprise data platformThe engineer can clearly explain what was built, why it matters, and how it supports the businessIdeal Candidate ProfileThe 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.