Torchora
Back to Home

data processor

title: Key Responsibilities and Required Skills for Data Processor
salary: $40,000 - $70,000
categories: [Data, Analytics, Operations, IT]
description: A comprehensive overview of the key responsibilities, required technical skills and professional background for the role of a Data Processor.
Hiring a Data Processor: Clear, actionable responsibilities and required skills for candidates experienced in data processing, ETL, data cleansing, SQL, Excel, scripting and data quality assurance. This role focuses on transforming raw data into accurate, consumable datasets, automating operational workflows, ensuring compliance with data governance, and partnering with analytics and business teams to deliver timely insights. Ideal for candidates with strong attention to detail, database experience, and a continuous improvement mindset.

🎯 Role Definition

The Data Processor is responsible for ingesting, cleaning, transforming, validating and delivering operational and analytical data sets. This role ensures high data quality, repeatable ETL/ELT processes, and accurate delivery to downstream consumers (analytics, reporting, BI, ML). The Data Processor drives process standardization, implements automation to reduce manual effort, enforces data governance and collaborates with stakeholders across business and IT to meet SLAs and compliance requirements.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Entry Clerk transitioning to structured data operations.
  • Junior Data Analyst with hands-on ETL and reporting exposure.
  • Business Operations Associate experienced in Excel and process automation.

Advancement To:

  • Senior Data Processor / Data Engineer
  • ETL Developer / Data Integration Specialist
  • Data Quality Analyst / Data Governance Analyst

Lateral Moves:

  • Business Intelligence (BI) Developer
  • Reporting Analyst
  • Operations Analyst

Core Responsibilities

Primary Functions

  • Execute daily and scheduled batch and streaming data ingestion tasks across multiple sources (CSV, APIs, databases, flat files), ensuring timely availability of data for analytics and operational systems.
  • Build, maintain, and monitor ETL/ELT pipelines using SQL, Python, R, or ETL tools (e.g., Talend, Informatica, Apache NiFi) to extract, transform and load data while documenting transformation logic and lineage.
  • Perform comprehensive data cleansing and normalization (de-duplication, standardization, format conversion, type casting) to prepare accurate datasets for reporting and downstream consumption.
  • Validate incoming data against defined business rules and schemas, implement automated data validation checks, and escalate anomalies to data owners with clear remediation steps.
  • Reconcile and balance datasets between source systems and data warehouses/data lakes using record counts, checksums, and sample audits to guarantee completeness and correctness.
  • Develop and maintain SQL queries, stored procedures, and views to support reporting, dashboards, and ad-hoc analytics requests with a focus on performance and maintainability.
  • Create reusable data transformation modules and templates to accelerate onboarding of new data sources and reduce time-to-value for analytics teams.
  • Implement logging, alerting and observability around data workflows (job failures, latency, throughput) to meet SLAs and reduce mean time to recovery (MTTR).
  • Automate manual data tasks by authoring scripts, macros, or small utilities (Python, Bash, PowerShell, VBA) to improve efficiency and reduce errors.
  • Ensure metadata, data dictionary entries, and source-to-target mappings are up to date and accessible to stakeholders to improve transparency and trust in data assets.
  • Collaborate with data engineers and platform teams to optimize storage formats, partitioning strategies, and indexing to improve query performance and cost efficiency.
  • Apply data privacy and compliance controls (PII masking, encryption, access controls) in accordance with GDPR, CCPA, or internal governance policies during processing and delivery.
  • Participate in root cause analysis of recurring data issues, propose corrective actions, and document long-term fixes in collaboration with engineering teams.
  • Transform business requirements into technical specifications and clearly document acceptance criteria, data transformations, and expected outputs for each data feed.
  • Conduct regular data quality profiling and trending analyses to identify systemic issues, measure data health metrics, and recommend process improvements.
  • Support migration and onboarding projects by mapping legacy data schemas, performing parallel runs, and validating target datasets during cutover phases.
  • Prepare and deliver clear, actionable reports and status updates to business stakeholders and project managers regarding data quality, processing schedules, and risk areas.
  • Maintain access controls and user provisioning for data environments, ensuring least-privilege access and secure handling of sensitive datasets.
  • Assist in the design and enforcement of naming conventions, folder structures, and version control practices for ingestion scripts and data artifacts.
  • Work with cross-functional teams (product, finance, marketing, operations) to prioritize data requests, scope deliverables, and provide realistic timelines for processing and delivery.
  • Evaluate and recommend new tools, libraries, or platform features that can improve data processing reliability, scalability, or reduce total cost of ownership.
  • Mentor junior data processors and provide training on standard procedures, quality assurance workflows, and best practices for data handling.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to help stakeholders derive immediate insights and answer urgent business questions.
  • Contribute to the organization's data strategy and roadmap by providing operational feedback, identifying gaps, and suggesting automation opportunities.
  • Collaborate with business units to translate data needs into engineering requirements and prioritize delivery by business impact.
  • Participate in sprint planning and agile ceremonies within the data engineering team to align work items with delivery timelines and quality expectations.
  • Assist in vendor evaluations and manage third-party data integrations, coordinating testing, SLA definitions and ongoing support.
  • Document and maintain runbooks and playbooks for common incidents and recovery steps to reduce downtime and streamline incident response.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong proficiency in SQL (complex joins, window functions, CTEs, performance tuning) for data extraction, transformation and validation.
  • Experience building and maintaining ETL/ELT pipelines using tools such as Airflow, Talend, SSIS, Informatica, or cloud-native schedulers.
  • Practical scripting skills in Python, Bash, PowerShell or R for automation, data wrangling and lightweight transformation tasks.
  • Advanced Microsoft Excel skills (pivot tables, VLOOKUP/XLOOKUP, macros) for quick reconciliations and reporting.
  • Familiarity with relational databases (MySQL, PostgreSQL, SQL Server, Oracle) and cloud data warehouses (BigQuery, Snowflake, Redshift).
  • Knowledge of data formats and serialization (CSV, JSON, Parquet, Avro) and experience converting and optimizing storage formats.
  • Experience with data quality tools and techniques (profiling, validation rules, anomaly detection, data lineage).
  • Basic understanding of cloud platforms and services (AWS, GCP, Azure) including storage, compute and managed data services.
  • Hands-on experience with version control (Git) and basic CI/CD concepts for deploying data pipelines and scripts.
  • Understanding of data governance, privacy controls, PII handling, masking and compliance requirements (GDPR, CCPA).
  • Familiarity with BI and visualization tools (Tableau, Power BI, Looker) to support data delivery and validation with consumers.
  • Knowledge of monitoring, alerting and logging tools (Prometheus, Datadog, ELK) to ensure operational reliability.

Soft Skills

  • High attention to detail and strong data accuracy mindset; able to spot anomalies and inconsistencies quickly.
  • Strong problem-solving and analytical thinking with the ability to diagnose data issues end-to-end.
  • Clear written and verbal communication skills for documenting processes and explaining technical issues to non-technical stakeholders.
  • Time management and prioritization skills to handle concurrent SLAs, ad-hoc requests and project work.
  • Collaborative team player who thrives in cross-functional environments and can negotiate realistic delivery timelines.
  • Customer-focused approach, proactively engaging with stakeholders to gather requirements and confirm acceptance criteria.
  • Adaptability and continuous learning mindset to adopt new tools, methods and industry best practices.
  • Patience and pedagogy for mentoring less experienced colleagues and producing clear training materials.

Education & Experience

Educational Background

Minimum Education:

  • Associate degree or relevant certification in Data Management, Computer Science, Information Systems, Business Analytics, or equivalent practical experience.

Preferred Education:

  • Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, or related field.

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Data Science / Analytics
  • Statistics / Mathematics
  • Business Analytics

Experience Requirements

Typical Experience Range:

  • 1–4 years of hands-on experience in data processing, ETL, data validation, or related operational data roles.

Preferred:

  • 3+ years working with SQL-based pipelines, automation scripting, and production data workflows; experience in cloud data environments (Snowflake, BigQuery, Redshift) is a strong plus.
  • Prior experience in regulated industries or environments with strict data governance and compliance requirements (finance, healthcare, e-commerce).
Explore More Careers