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Key Responsibilities and Required Skills for Data Software Engineer

💰 $110,000 - $170,000

Data EngineeringSoftware EngineeringCloudBig DataAnalytics

🎯 Role Definition

As a Data Software Engineer, you will design, build, and operate reliable, scalable data platforms and pipelines that power analytics, machine learning, and business decision-making. This role combines software engineering discipline with deep data expertise: producing production-grade ETL/ELT, enabling data products, ensuring data quality, and collaborating cross-functionally to turn raw data into actionable intelligence. Core responsibilities include architecting data solutions, implementing robust infrastructure-as-code and CI/CD practices, monitoring observability, and optimizing performance for large-scale distributed systems on cloud platforms (AWS/GCP/Azure).

📈 Career Progression

Typical Career Path

Entry Point From:

  • Software Engineer or Backend Engineer transitioning into data-centric systems
  • Data Engineer, ETL Engineer, or Analytics Engineer with production pipeline experience
  • Machine Learning Engineer or Data Scientist moving into production data infrastructure

Advancement To:

  • Senior Data Software Engineer / Staff Data Engineer
  • Lead Data Engineer or Data Platform Engineering Manager
  • Principal Engineer / Architect (Data Platform, Cloud Data Architecture)

Lateral Moves:

  • Machine Learning Platform Engineer
  • Data Architect or Solutions Architect
  • Analytics Engineering Lead

Core Responsibilities

Primary Functions

  • Design, implement, and maintain end-to-end data pipelines (batch and streaming) that ingest, transform, and deliver data for analytics, reporting, and machine learning workloads while meeting SLAs for latency, throughput, and reliability.
  • Architect and build robust ETL/ELT processes using tools such as dbt, Apache Spark, Apache Beam, or custom Python/Scala code; ensure transformations are testable, modular, and version controlled.
  • Develop production-grade data services and APIs that expose curated datasets to downstream consumers, ensuring secure access patterns and consistent schema governance.
  • Own the scalability and performance tuning of distributed data systems, optimizing Spark jobs, SQL queries, partitioning strategies, and cluster resources to reduce cost and improve throughput.
  • Design and operate streaming data architectures using Kafka, Kinesis, Pub/Sub, or Pulsar, including topic design, exactly-once/at-least-once semantics, backpressure handling, and consumer scaling.
  • Implement data modeling and schema design for analytical and dimensional models; partner with analytics teams to ensure datasets align to business semantics and KPIs.
  • Build and maintain infrastructure-as-code for data platform components using Terraform/CloudFormation and enforce environment parity across development, staging, and production.
  • Implement CI/CD pipelines for data code and infrastructure, ensuring automated testing, linting, schema checks, and safe deployment practices for database migrations and pipeline changes.
  • Establish data quality monitoring, validation rules, and observability for pipelines using tools like Great Expectations, Deequ, Monte Carlo, or custom validation frameworks; define alerts and remediation processes.
  • Collaborate with data scientists and ML engineers to productionize feature stores, model data pipelines, and ensure reproducible training and inference data flows.
  • Maintain strong data lineage, metadata, and cataloging practices so that data provenance and ownership are clear; integrate with tools such as Amundsen, DataHub, or Glue Data Catalog.
  • Lead root-cause analysis for production incidents, write postmortems, and implement preventative measures; own incident response for data failures and pipeline regressions.
  • Implement access controls, encryption, key management, and IAM policies to protect sensitive data and meet compliance requirements (GDPR, CCPA, HIPAA as applicable).
  • Create and maintain comprehensive documentation for data contracts, schemas, pipeline architecture, runbooks, and onboarding materials for internal engineering and analytics teams.
  • Mentor and coach junior engineers on best practices in testing, code quality, debugging distributed systems, and data engineering principles.
  • Integrate monitoring, logging, metrics, and tracing for data jobs and services using Prometheus, Grafana, CloudWatch, Stackdriver/Cloud Logging, Elastic, or Datadog to provide actionable insights.
  • Evaluate, prototype, and recommend new data processing technologies and cloud-native services to improve developer productivity, cost-efficiency, and time-to-insight.
  • Build reusable libraries and frameworks to standardize data ingestion, transformation, and testing across multiple teams and domains.
  • Collaborate with product managers and business stakeholders to translate data requirements into technical deliverables, prioritize roadmap items, and estimate engineering effort.
  • Implement backup, recovery, and retention policies for critical datasets; design strategies for disaster recovery and cross-region replication where necessary.
  • Ensure reproducibility and portability of data workloads by containerizing applications with Docker and orchestrating jobs with Kubernetes, Airflow, or managed workflow services.

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 programming skills in Python and/or Scala for data processing, unit testing, and automation; familiarity with typed languages (Java/Go) is a plus.
  • Expert-level SQL skills for complex analytical queries, performance tuning, and working with columnar stores (Redshift, BigQuery, Snowflake, ClickHouse).
  • Experience building and optimizing Spark or distributed compute jobs (PySpark, spark-submit, Spark SQL) for large-scale ETL/ELT workloads.
  • Hands-on experience with cloud data platforms and managed services (AWS: EMR, Glue, Redshift, S3; GCP: Dataflow, BigQuery, Pub/Sub; Azure: Databricks, Synapse).
  • Proficiency with streaming frameworks and message brokers (Apache Kafka, Kinesis, Pulsar) and familiarity with stream processing patterns.
  • Infrastructure-as-code and cloud provisioning experience (Terraform, CloudFormation) to deploy and manage data infrastructure.
  • Workflow orchestration experience with Airflow, Prefect, Dagster, or similar systems to schedule and manage complex DAGs.
  • Data modeling and schema design expertise: star/snowflake schemas, normalized vs. denormalized, partitioning, and indexing strategies.
  • Data quality, testing, and validation skills using Great Expectations, Deequ, or custom testing frameworks; implement CI for data pipelines.
  • Observability and monitoring skills including metrics, logging, tracing (Prometheus, Grafana, ELK stack, Datadog) and alerting practices.
  • Containerization and orchestration experience (Docker, Kubernetes) for deploying data services and batch/stream workers.
  • Familiarity with columnar storage, compression, and file formats (Parquet, Avro, ORC) and best practices for data lakes and lakehouses.
  • Knowledge of security, encryption, IAM policies, and compliance considerations for sensitive data handling.
  • Experience with version control and collaborative workflows (Git, GitHub/GitLab) and code review best practices.
  • Understanding of ML data pipelines, feature stores, and MLOps concepts to support model training and deployment.

Soft Skills

  • Strong verbal and written communication skills to explain technical designs to both technical and non-technical stakeholders.
  • Collaborative mindset with experience working cross-functionally with product, analytics, data science, and operations teams.
  • Problem-solving and analytical thinking with a bias for data-driven decision making and pragmatic tradeoffs.
  • Ownership and accountability for production systems, incident response, and long-term maintainability.
  • Ability to mentor junior engineers and contribute to a culture of continuous learning and improvement.
  • Time management and prioritization skills to balance multiple concurrent initiatives and stakeholders.
  • Curiosity and adaptability to evaluate new technologies, patterns, and best practices in a rapidly evolving data ecosystem.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Computer Science, Data Engineering, Machine Learning, or related discipline.
  • Relevant industry certifications (AWS Certified Data Analytics, GCP Professional Data Engineer, Databricks Certification) are a plus.

Relevant Fields of Study:

  • Computer Science or Software Engineering
  • Data Science, Statistics, or Applied Mathematics
  • Information Systems, Cloud Engineering, or Applied Physics

Experience Requirements

Typical Experience Range:

  • 3–8+ years of experience building production data systems, software engineering, or data engineering roles.

Preferred:

  • 5+ years of hands-on experience architecting and operating scalable data pipelines on cloud platforms, with demonstrated ownership of production data services and cross-functional collaboration.
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