Torchora
Back to Home

Key Responsibilities and Required Skills for Loader (Data / ETL)

💰 $ - $

Data EngineeringETLData IntegrationAnalytics

🎯 Role Definition

The Loader (Data / ETL) is a hands-on data engineering practitioner responsible for designing, building, operating, and optimizing data ingestion and transformation pipelines. This role focuses on reliably moving data from multiple source systems into analytics-ready data stores (data lake, data warehouse, marts), ensuring data quality, observability, cost efficiency, and SLA compliance. The Loader works closely with data engineers, analytics engineers, data scientists, product owners, and business stakeholders to translate data requirements into robust ETL/ELT solutions using SQL, Python, orchestration tools (Airflow, Prefect), and cloud platforms (AWS, GCP, Azure).

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer or ETL Developer
  • Data Analyst with scripting/SQL experience
  • BI Developer or Database Administrator

Advancement To:

  • Senior Data Engineer / ETL Architect
  • Analytics Engineer / Data Platform Engineer
  • Data Engineering Lead or Manager

Lateral Moves:

  • Business Intelligence (BI) Developer
  • Data Analyst / Reporting Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and maintain scalable ETL/ELT pipelines to ingest structured and semi-structured data from transactional systems, APIs, streaming sources, and third-party providers into the central data platform.
  • Author performant SQL transformations and stored procedures to prepare raw data for analytics, applying best practices for partitioning, indexing, and query optimization in Snowflake, Redshift, BigQuery, or other warehouses.
  • Build robust data extraction logic (batch and incremental) with change-data-capture (CDC) patterns, timestamp/CDC windowing, and idempotent loaders to avoid duplicates and ensure correctness.
  • Implement data loading processes using Python, SQL, Scala, or ETL tools (Airflow, Prefect, Talend, Fivetran, Stitch, Informatica), and package pipelines for repeatable deployments.
  • Orchestrate complex workflows and dependencies with Airflow, Prefect, or cloud-native schedulers; write DAGs, sensors, operators and implement retry, backoff, and alerting strategies.
  • Instrument pipelines with logging, metrics (Prometheus/Grafana), and tracing so SLAs and throughput are observable; maintain monitoring dashboards and runbooks for on-call incidents.
  • Build automated data quality checks, anomaly detection rules, and validation tests (null checks, schema evolution, referential integrity) to ensure data reliability for downstream consumers.
  • Maintain and evolve schema management, data contracts, and data lineage (using tools like dbt, Great Expectations, Monte Carlo, or open-source metadata solutions) to provide auditability and change awareness.
  • Optimize data storage, partitioning, clustering, and compute usage to control cloud costs and improve query performance across the data platform.
  • Implement secure data handling and access controls (IAM, RBAC, column masking, encryption in transit and at rest) and ensure compliance with data privacy regulations (GDPR, CCPA) and internal policies.
  • Integrate with source system owners to perform data mapping, reconciliation, and source-to-target documentation; manage onboarding of new data sources end-to-end.
  • Create and maintain clear technical documentation, runbooks, deployment guides, and data dictionaries to support reproducible builds and team knowledge transfer.
  • Participate in code reviews, enforce CI/CD practices for ETL code (unit tests, integration tests, Terraform/CloudFormation for infra), and maintain versioned deployments.
  • Troubleshoot and perform root cause analysis on pipeline failures, execute remediation actions, and implement long-term fixes to prevent recurrence.
  • Implement incremental development and feature flags to enable safe rollouts of new loaders and transformations with minimal disruption to consumers.
  • Collaborate with analytics and data science teams to build derived datasets, feature stores, and aggregated tables that power dashboards, ML models, and reports.
  • Perform capacity planning and load testing for high-volume ingestion scenarios; advise on partitioning, sharding, and parallelization strategies to scale loaders.
  • Automate repetitive operational tasks (cleanup, archival, retention policies) and ensure retention and lifecycle management for raw and curated datasets.
  • Mentor junior loaders and data engineers on best practices for ETL development, pipeline observability, and performance tuning.
  • Manage SLA-driven priorities for data delivery, coordinate cutovers for schema changes, and communicate status to stakeholders during incidents and planned maintenance.
  • Evaluate and recommend new ingestion tools, managed services, or ETL frameworks (e.g., dbt, Airbyte, Matillion) to improve developer productivity and platform reliability.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong SQL skills: complex joins, window functions, CTEs, query optimization, and query plan analysis on modern cloud warehouses (Snowflake, Redshift, BigQuery).
  • Proficient in Python (or Scala/Java) for ETL scripting, data transformations, API integrations, and writing data pipeline components.
  • Experience building and maintaining Airflow, Prefect, or similar orchestration DAGs; knowledge of DAG design patterns, sensors, and custom operators.
  • Familiarity with ETL/ELT tools and SaaS connectors (Fivetran, Stitch, Matillion, Talend, Informatica) and when to use managed services vs. custom loaders.
  • Experience with cloud platforms and services (AWS — S3, Glue, EMR, Lambda; GCP — Cloud Storage, Dataflow; Azure — Blob, Data Factory).
  • Hands-on experience with cloud data warehouses and lakes (Snowflake, Redshift, BigQuery, Delta Lake) and knowledge of storage formats (Parquet, Avro, ORC).
  • Data modeling skills: star/snowflake schema design, dimensional modeling, and best practices for analytics-ready datasets.
  • Familiarity with testing frameworks for data (Great Expectations, dbt tests), unit/integration testing, and implementing data quality assertions.
  • Knowledge of monitoring and observability tools (Prometheus, Grafana, DataDog, Sentry) and experience building operational dashboards and alerts.
  • Understanding of CI/CD for data engineering (GitOps, Terraform, Docker, Kubernetes) and automated deployment pipelines.
  • Experience implementing data governance, access controls (IAM/RBAC), encryption, and compliance controls for sensitive datasets.
  • Ability to use version control (Git) effectively and follow branching and review workflows.
  • Familiarity with streaming ingestion frameworks (Kafka, Kinesis) and real-time processing patterns is a plus.
  • Knowledge of cost optimization techniques for cloud compute and storage (auto-scaling, pause/resume, warehouse sizing).

Soft Skills

  • Strong problem-solving and analytical thinking with attention to detail and data accuracy.
  • Excellent communication skills — able to translate technical constraints to non-technical stakeholders and write clear documentation.
  • Collaborative mindset: works cross-functionally with product, analytics, and engineering teams to deliver prioritized data capabilities.
  • Prioritization and time management: able to balance operational incident response with feature development.
  • Ownership and accountability for SLAs, quality metrics, and day-to-day reliability of data pipelines.
  • Adaptability and willingness to learn new tools and paradigms in a fast-evolving data stack.
  • Mentorship and coaching skills to help junior team members grow technical abilities.
  • Strong bias for testing, automation, and reproducible processes.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, Mathematics, Statistics, or equivalent professional experience. Equivalent hands-on ETL and data engineering experience will be considered.

Preferred Education:

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or related technical field.
  • Certifications in cloud platforms (AWS/GCP/Azure) or data tools (Snowflake, dbt) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Data Science
  • Software Engineering
  • Mathematics / Statistics

Experience Requirements

Typical Experience Range: 1–5 years of hands-on experience building and operating ETL/ELT/data ingestion pipelines.

Preferred: 3+ years of experience with cloud data warehouses (Snowflake, BigQuery, Redshift), orchestration tools (Airflow/Prefect), and scripting (Python/SQL) in production environments; experience with CI/CD and data quality tooling preferred.

Explore More Careers