Senior Data Integrations Engineer to support the analysis, design, and documentation of enterprise

Position Overview

We are seeking a Senior Data Integrations Engineer to support the analysis, design, and documentation of enterprise data integration solutions. The successful candidate will work closely with data engineering, data architecture, and business teams to analyze source systems, assess data requirements, and develop detailed Source-to-Target Mapping (STTM) documentation to support data pipeline development.

This role requires strong expertise in SQL, data profiling, dimensional data modeling, Data Vault methodology, impact analysis, and data transformation logic. The ideal candidate will have experience working with complex data environments and translating business requirements into clear, accurate technical specifications for engineering teams.

Number of openings - 2

Duration - 1 year

Start - ASAP

Work Mode - Remote

Key Responsibilities

Data Analysis & Profiling

• Analyze and profile data from multiple source systems to understand data structures, relationships, and quality.
• Identify data gaps, inconsistencies, anomalies, and potential data quality issues.
• Perform detailed SQL queries to validate source data and support transformation requirements.
• Collaborate with business and technical stakeholders to investigate data discrepancies and recommend solutions.
• Document source data findings and provide actionable insights to support downstream data integration activities.

Source-to-Target Mapping (STTM) & Transformation Design

• Develop, maintain, and validate comprehensive Source-to-Target Mapping (STTM) documentation for enterprise data integration pipelines.
• Translate business and technical requirements into detailed data mapping specifications and transformation rules.
• Define transformation logic based on target data models, including data cleansing, standardization, aggregation, and validation rules.
• Apply dimensional modeling techniques, including fact tables, dimension tables, star and snowflake schemas, and Slowly Changing Dimensions (SCD).
• Leverage Data Vault modeling concepts, including hubs, links, and satellites, to define transformation logic where applicable.
• Ensure STTM documentation is accurate, complete, and aligned with enterprise data architecture standards.
• Work closely with data engineers to clarify mapping requirements and support successful pipeline implementation.

Impact Analysis & Feasibility Assessment

• Conduct detailed impact assessments to determine how new business requirements affect existing data models, integrations, and source systems.
• Identify dependencies, potential risks, and required changes to existing data integration designs.
• Evaluate the technical feasibility of new data requirements within the existing system architecture.
• Recommend alternative approaches when requirements cannot be supported by current data models or integration frameworks.
• Document findings, technical considerations, and recommended solutions for business and technology stakeholders.

Data Quality & Collaboration

• Collaborate with data architects, data engineers, business analysts, and data governance teams to ensure consistency across data integration solutions.
• Validate data mapping specifications and transformation rules against business requirements.
• Support data quality assessments and contribute to the development of data validation rules.
• Assist engineering teams in troubleshooting data mapping and transformation issues.
• Maintain detailed technical documentation, including data mappings, transformation specifications, impact assessments, and data analysis findings.
• Ensure alignment with enterprise data governance, metadata management, and data quality standards.

Required Qualifications & Technical Skills

• Proven experience in Data Integration, Data Analysis, Data Engineering, or Data Mapping within complex enterprise environments.
• Strong hands-on experience with Source-to-Target Mapping (STTM) documentation and data transformation specifications.
• Advanced SQL skills, including complex queries, joins, aggregations, and data profiling across large datasets.
• Strong understanding of dimensional data modeling, including fact and dimension tables, star/snowflake schemas, and Slowly Changing Dimensions (SCD).
• Working knowledge of Data Vault modeling, including hubs, links, satellites, and associated transformation logic.
• Demonstrated experience performing data profiling, data quality assessments, and gap analysis across multiple source systems.
• Experience conducting impact analysis and feasibility assessments for new data integration requirements.
• Strong understanding of data integration concepts, ETL/ELT processes, data warehousing, and data transformation methodologies.
• Ability to interpret complex business requirements and translate them into detailed technical data mapping specifications.
• Experience working with data architects and engineering teams to support enterprise data integration initiatives.
• Excellent analytical, troubleshooting, and problem-solving skills.
• Strong technical documentation skills and attention to detail.
• Excellent communication and stakeholder management skills, with the ability to explain complex data concepts to technical and non-technical audiences.

Nice-to-Have Qualifications

• Experience with cloud-based data platforms such as Snowflake or Databricks.
• Familiarity with cloud data architectures and modern data integration frameworks.
• Exposure to data governance, data lineage, metadata management, and data cataloguing tools.
• Experience with automated data profiling and data quality frameworks.
• Knowledge of enterprise data warehouse architecture and modern data lakehouse environments.
• Experience working in Agile delivery environments and collaborating with cross-functional data teams.

Ideal Candidate Profile

The ideal candidate is a technically strong Data Integrations Engineer / Data Mapping Specialist with extensive experience analyzing complex source data, defining transformation logic, and producing detailed Source-to-Target Mapping documentation.

They should possess advanced SQL capabilities, strong dimensional modeling knowledge, and practical experience with Data Vault methodologies.

The successful candidate will be able to independently assess data requirements, identify potential integration challenges, and deliver accurate technical specifications that enable data engineering teams to develop reliable, scalable, and high-quality data pipelines.

Key Success Factors

• Identify and communicate source data issues early to minimize downstream development risks.
• Ensure new requirements are assessed for technical feasibility and impact before development begins.
• Deliver accurate, comprehensive, and actionable STTM documentation.
• Define transformation logic aligned with dimensional and Data Vault modeling best practices.
• Enable efficient and reliable data pipeline development through clear technical specifications and collaboration.