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Key Responsibilities and Required Skills for Data Developer

💰 $ - $

DataEngineeringAnalytics

🎯 Role Definition

A Data Developer designs, builds, tests, and maintains robust, scalable data pipelines and data products that enable analytics, reporting, and downstream machine learning. The role focuses on ETL/ELT development, data modeling, performance optimization, data quality, and collaboration with analytics and product teams to deliver trusted, timely, and cost-effective data solutions across cloud and on-premise environments.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer or Data Analyst transitioning into engineering-focused pipeline development.
  • Software Engineer or Backend Developer with an interest in data platforms.
  • BI Developer or Analytics Engineer moving into broader data engineering responsibilities.

Advancement To:

  • Senior Data Developer / Senior Data Engineer
  • Lead Data Engineer or Engineering Manager (Data)
  • Data Architect or Principal Data Engineer
  • Analytics Engineering Manager or Head of Data Platform

Lateral Moves:

  • Machine Learning Engineer (with feature engineering focus)
  • Analytics Engineer / BI Engineer
  • Data Product Manager

Core Responsibilities

Primary Functions

  • Design, develop, and maintain end-to-end ETL/ELT data pipelines using SQL, Python, Spark, and modern orchestration tools (e.g., Airflow) to transform raw data into analytics-ready datasets and data products for BI and ML consumption.
  • Implement and own batch and streaming data ingestion processes from a variety of sources (APIs, databases, message queues like Kafka, file systems, and cloud storage) ensuring reliability, latency targets, and schema evolution handling.
  • Build and evolve dimensional and normalized data models, creating star schemas, conformed dimensions and facts, and canonical data representations to support reporting, analytics, and machine learning workflows.
  • Develop and maintain production-grade SQL and Spark jobs optimized for performance and cost on cloud data warehouses and processing platforms such as Snowflake, Redshift, BigQuery, Databricks, or equivalent.
  • Implement ELT patterns and transformations using modern tooling (dbt, Spark, SQL-based transformations) to enforce version-controlled, testable, and documented data transformations.
  • Author comprehensive unit, integration and data quality tests (including dbt tests or similar frameworks), and automate testing within CI/CD pipelines to prevent data regressions and ensure release stability.
  • Create and maintain orchestration workflows using Airflow, Prefect, or similar schedulers; manage DAGs, dependencies, retry strategies, and alerting to ensure timely data availability.
  • Monitor pipeline health, set up robust observability (metrics, logs, lineage), and implement automated alerting and incident playbooks to minimize downtime and quickly triage production issues.
  • Implement data validation, anomaly detection, and data quality frameworks to measure completeness, accuracy, freshness, and uniqueness of datasets, and remediate issues in collaboration with data stewards.
  • Collaborate with product managers, data analysts, and data scientists to gather requirements, scope features, and translate business needs into technical solutions with clear SLAs and acceptance criteria.
  • Establish and maintain data contracts and APIs for downstream consumers, ensuring schema compatibility, versioning strategies, and clear service-level agreements for data access.
  • Participate in architecture and design reviews, providing expertise on trade-offs between real-time vs. batch processing, storage costs, and scalability to guide platform decisions.
  • Implement secure data handling practices, including encryption, masking, and access controls, and work with security/compliance teams to ensure adherence to GDPR, HIPAA, SOC2, or other relevant regulations.
  • Maintain and enrich data catalogs and metadata systems (e.g., Amundsen, DataHub, Collibra), documenting dataset lineage, ownership, definitions, and usage patterns to enable data discoverability and governance.
  • Optimize query performance and cost by tuning warehouse resource allocation, partitioning, clustering, and query patterns, and by providing guidance to analysts on efficient data access patterns.
  • Develop reusable data engineering patterns, templates, and libraries to accelerate development and ensure consistency across teams, including standardized logging, error handling, and retry mechanisms.
  • Support on-call rotations and incident response for critical data systems, performing root cause analysis and implementing long-term fixes to prevent recurrence.
  • Drive data migration and modernization efforts such as lift-and-shift to cloud warehouses, consolidation of legacy pipelines, or adoption of new processing frameworks with minimal business disruption.
  • Collaborate with data science teams to productionize features and ML feature stores, ensuring reproducibility, freshness, and low-latency access for model training and serving.
  • Contribute to cost-management initiatives for cloud data platforms by implementing lifecycle policies, archival processes, and query optimization best practices.
  • Mentor junior engineers and analysts on data engineering best practices, code reviews, and design patterns to raise team capability and code quality.
  • Create clear technical documentation, runbooks, and onboarding materials that communicate architecture decisions, dependencies, and operational processes to non-engineering stakeholders.
  • Evaluate and recommend new data tools, libraries, and architectural patterns, running proof-of-concepts and providing ROI analyses for adoption or deprecation decisions.

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)

  • Advanced SQL: complex joins, window functions, CTEs, query performance tuning and explain-plan analysis.
  • Python (or Scala/Java): building ETL/ELT, data transformations, unit testing, and automation scripts.
  • Big data frameworks: Apache Spark (PySpark/Scala), Databricks, or similar distributed processing platforms.
  • Cloud data platforms: hands-on experience with Snowflake, BigQuery, AWS Redshift, Azure Synapse, or equivalent.
  • Orchestration and scheduling: Apache Airflow, Prefect, Luigi, or cloud-native schedulers.
  • Data modeling and warehousing: dimensional modeling, star/snowflake schemas, normalization, slowly changing dimensions (SCD).
  • ELT tooling and analytics engineering: dbt (data build tool), schema management, and test frameworks.
  • Streaming and messaging systems: Kafka, Kinesis, Pub/Sub, or other real-time ingestion technologies.
  • Data quality, validation and observability: Great Expectations, Monte Carlo, Deequ, custom checks, and monitoring solutions.
  • CI/CD and version control: Git, GitHub/GitLab, automated testing, and deployment pipelines.
  • APIs and data access: RESTful APIs, GraphQL, JDBC/ODBC, and data connectors.
  • Metadata and governance tools: Amundsen, DataHub, Collibra, or internal catalog solutions.
  • Containerization and infra basics: Docker, Kubernetes (desirable) and familiarity with IaC (Terraform, CloudFormation) for data platform provisioning.
  • SQL-based BI tools familiarity: Looker, Tableau, Power BI; ability to collaborate with analysts on dataset design.

Soft Skills

  • Strong problem-solving and troubleshooting mindset with attention to detail in production systems.
  • Excellent communication: translate technical trade-offs in business terms and present to cross-functional stakeholders.
  • Collaborative team player: work closely with product, analytics, data science and operations teams.
  • Time management and prioritization: manage multiple pipelines, incidents, and projects with competing deadlines.
  • Mentorship and knowledge sharing: coach junior engineers, run brown-bags, and contribute to engineering best practices.
  • Customer-first orientation: understand consumer needs (analysts, data scientists) and deliver usable data products.
  • Adaptability: quickly learn new tools and pivot solutions as platforms and business priorities evolve.
  • Documentation and process orientation: produce clear runbooks and design docs to support operational reliability.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Mathematics, Statistics, or a closely related field (or equivalent professional experience).

Preferred Education:

  • Master's degree in Data Science, Computer Science, or Engineering, or certifications in cloud platforms (AWS/GCP/Azure), dbt, or data engineering.

Relevant Fields of Study:

  • Computer Science
  • Data Engineering / Data Science
  • Software Engineering
  • Statistics, Mathematics, Applied Mathematics
  • Information Systems / Business Analytics

Experience Requirements

Typical Experience Range:

  • 2–6 years of hands-on experience building and operating data pipelines, data warehouses, and data transformation platforms.

Preferred:

  • 4–8+ years with demonstrated ownership of production data systems, cloud data platform migrations, and leadership in data engineering projects; experience with Snowflake/BigQuery/Redshift, Spark, Airflow and dbt strongly preferred.
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