[Job - 26644] Senior Data Analyst – Data Migration & Process Automation

We are tech transformation specialists, uniting human expertise with AI to create scalable tech solutions. With over 8,000 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality. We are looking for a Data Analyst with strong experience in data migration, transformation, and process automation. You will be responsible for analyzing gaps between our source data and target databases, designing transformation and enrichment rules, and helping us build a robust, automated data import pipeline. The main focus of this role is to ensure that data arriving primarily from CSV files is correctly transformed, loaded via SQL into our final databases, and remains consistent and reliable for the systems that consume it. What You Will Do 1. Data Gap & Requirements Analysis Analyze existing data sources (mainly CSV files and source databases) and target database schemas. Identify gaps, inconsistencies, and data quality issues between source and target. Map fields between source and destination systems, including complex mappings and business rules. Document data definitions, data flows, and dependencies. 2. Data Transformation, Enrichment & Cleaning Define and document rules for data transformation, enrichment, validation, and filling missing values. Work with stakeholders to clarify business logic for derived and enriched fields. Design and test transformation logic (e.g., standardization, normalization, lookups, reference data enrichment). 3. Data Import & Automation Translate transformation rules into SQL-based imports and scripts/processes that load data into the final databases. Collaborate with engineers to design and/or implement automated ETL/ELT pipelines for regular data loads from CSV to the target databases. Define and help implement checks that ensure data consistency in the final system that consumes the databases. 4. Data Quality & Consistency Assurance Define data validation rules, quality checks, and reconciliation reports (e.g., record counts, referential integrity, duplicates, null checks). Monitor imports and identify root causes of issues in the ingestion process. Propose improvements to schemas, processes, and tools to minimize manual interventions and make the process as straightforward and reliable as possible. 5. Documentation & Collaboration Create clear documentation of mappings, transformation rules, and automated processes. Work closely with product, engineering, and business stakeholders to refine requirements and validate outcomes. Communicate findings and recommendations in a clear, structured way to technical and non‑technical audiences. What We’re Looking For Must-Haves Proven experience as a Data Analyst, Data Migration Analyst, or similar role. Strong proficiency in SQL (complex joins, window functions, data profiling, validations). Hands-on experience working with CSV-based data imports and relational databases. Strong data modeling and mapping skills (understanding of schemas, relationships, primary/foreign keys). Experience designing data transformation and enrichment rules and documenting them clearly. Experience with data quality practices: validation rules, reconciliation, anomaly detection. Ability to translate business requirements into technical data requirements and processes. Strong analytical skills and attention to detail. Nice-to-Haves Experience with ETL/ELT tools or scripting for automation (e.g., Python, dbt, Airflow, SSIS, etc. – customize as needed). Experience in data migration projects between systems or platforms. Familiarity with data warehousing concepts and best practices. Experience with version control (e.g., Git) for SQL scripts and data pipelines. Exposure to the domain of [your industry, e.g., finance, e‑commerce, SaaS], especially data flows in that context. Soft Skills Strong facilitation and communication skills: able to lead and structure meetings that involve both business stakeholders and technical teams. Ability to interview and collaborate with people currently involved in manual processes, understand how they work today, and translate that into clear data and process requirements. Comfortable challenging assumptions and asking clarifying questions to uncover hidden business rules and edge cases. Ability to explain complex data and technical topics in simple, business‑friendly language, and to translate business needs into actionable technical tasks. Proactive, ownership mindset: drives discussions forward, follows up on decisions, and ensures alignment between business and technical perspectives. Organized and structured, able to keep track of decisions, open points, and next steps from workshops and meetings.