Introduction

Landing your first data engineering role—or starting at a new company—is both exhilarating and daunting. After navigating multiple interviews and accepting an offer, you’ve finally arrived at your desk with a new laptop and company swag (if your lucky).

Even now, after solving countless problems ranging from minor bugs to enterprise-scale data challenges, I still occasionally feel that flutter of uncertainty in my stomach, when starting a new role. What if I don’t know what I’m doing? What if I make a mistake?

One powerful way to combat this imposter syndrome is to approach your new role with a structured plan, a roadmap for your first 90 days. Most companies have a 90 day probationary period, so this provides a clear timeframe that naturally divides your onboarding journey into three distinct phases.

Understanding how to deliver impact as a data engineer is crucial for your success. In this role, you’ll create value in multiple ways—from measurable improvements like pipeline efficiency and cost savings to harder-to-measure contributions like maintaining data quality and enabling your colleagues. Having a framework to showcase this impact will help you navigate your early days and set you up for long-term career growth.



My Experience: A Financial Services Case Study

One experience came early in my career when I transition from a sister company to the main company. Prior to this role, I had worked in a retail bank’s sales team, but the newer team was more corporate and it was definitely a new set of people to work with, and different data to understand.

During this process one of the skip managers asked me “How will you get up to speed not just on our technology and codebase, but also on business knowledge and data?”

Here’s the approach I took:

  • Industry immersion: Read about financial markets, trading data, and key metrics like risk-adjusted returns and portfolio performance
  • Business process mapping: Quickly learned how data flowed through the organization, how transactions were tracked, and the key operational workflows
  • Relationship building: Initiated conversations with colleagues across the organization to understand the bigger picture
  • Technical exploration: Systematically went through the codebase, mapping objects and methods to understand the architecture
  • Value creation: Built a dashboard to track and automate financial reporting that previously required manual effort
  • Immediate contribution: Delivered small code fixes while handling day-to-day operations of the analytics data product

This experience helped me develop a more systematic methodology for approaching new roles.



First 30 Days: Observer to Navigator

The first month isn’t about producing massive output or making dramatic changes. Instead, focus on absorbing information, building relationships, and developing a comprehensive understanding of your new environment. During this phase, you’re laying the groundwork to understand how your role as a data engineer will translate into value for the organization.

Connect With Your Team and Stakeholders

No matter how technical your role, your effectiveness ultimately depends on your ability to channel that expertise toward solving real business problems. Building relationships early helps you understand those problems more deeply.

  • Set up 1:1 meetings: Schedule conversations with immediate team members, cross-functional colleagues, and key stakeholders. When I started at at a financial data team, I immediately set up a dozen 1:1s across different peers and colleagues.

  • Establish skip-level connections: Don’t limit yourself to peers and direct managers. Reach out to wider people to understand the broader context and strategic priorities. These relationships help you see beyond the immediate technical work to grasp why certain initiatives matter.

  • Request code walkthroughs: Ask experienced team members to walk you through critical components of the codebase. This provides immediate technical context and opens the door for valuable mentoring relationships.

The key here is to listen, ask questions and show your euthusastic to learn and get started. Whilst also building relationships that will help later on. Don’t offer a plethora of suggestions or ways to improve things - insted listen with intent and curiosity, understand why people have done things a certain way. The time for suggesting will be later, once you have a stronger foundation of trust with the team.

Understand the Code

Much of what I’ve learned about programming has come from studying well-developed codebases. Here’s how to approach this systematically:

  • Review documentation: Before diving into code, thoroughly read through READMEs, confluence/wikis, and any onboarding materials. Take notes on gaps or outdated information—updating these later provides immediate value.

  • Map the architecture: Create visual representations of how key classes, functions, and configurations interact. You won’t capture everything, but even a partial map significantly accelerates your understanding.

  • Study data flows: Track how data moves through various systems, including databases, APIs, and third-party integrations. This perspective often reveals insights that aren’t apparent from studying the code alone.

  • Analyze pull requests: Recent PRs offer insights into current development priorities, coding standards, and system evolution. Pay attention to comments and discussions to understand team dynamics.

  • Participate in code reviews: Even if you’re not yet comfortable making substantial comments, observing what others focus on teaches you implicit team standards and practices.

  • Fix simple bugs: Look for low-priority bug tickets that have been overlooked. Fixing these forces you to navigate the codebase while making tangible contributions.



60 Days: Building Momentum and Credibility

By the second month, you should have a solid grasp of the fundamentals. Now it’s time to deepen your understanding and take on more substantive work.

  • Identify knowledge gaps: Create a list of areas where your understanding remains incomplete. Perhaps there’s business logic you don’t fully grasp or process rationales that seem unclear. Now is the time to ask questions before expectations increase.

  • Take on complex tasks: Move beyond simple bug fixes to tasks requiring actual design thinking. Start leading technical discussions and contributing to architecture decisions.

  • Provide meaningful code reviews: Your perspective has value—especially as someone with fresh eyes. Offer substantive feedback during code reviews, balancing questions with constructive suggestions.

  • Improve documentation: Update outdated documentation or create new resources for areas lacking clarity. This serves multiple purposes: confirming your understanding, contributing immediate value, and helping future team members.

  • Focus on data quality: Begin establishing standards and processes that maintain trust in your data. While this impact is often undervalued because it’s harder to measure, it’s critical to the long-term success of your data platform. Quality checks, clear documentation, and good engineering practices are all part of this effort.

  • Schedule progress check-ins: Meet regularly with your manager to discuss your progress, address challenges, and calibrate expectations. This ensures you’re on track and provides opportunities for course correction if needed.

This is a key part to continue building trust within the team, but also now showing the value that you can bring. Building resiliance and quality into the existing streams of work (directly - pipeline fixes or improvements or indirectly - with code reviews), is a great way of showing your value and building the relationships in the team. You showing that you care about the detail, but also reducing the stress or burnout in the team, supporting pipelines that fail.



90 Days: Value multiplier

By the third month, you’ve developed enough context to start thinking bigger and identifying opportunities for meaningful impact. This is when you can begin to deliver value across different dimensions.

Types of Impact You Can Drive

As a data engineer, your impact generally falls into three categories:

  • Measurable impact: Pipeline efficiency improvements, cost savings, experiment outcomes
  • Hard-to-measure impact: Quality improvements, data trust maintenance, team enablement
  • Immeasurable impact: Changing intuitions, improving team culture, being a “glue” person

With this in mind, here are key actions to take during this phase:

  • Propose new initiatives: Leverage your fresh perspective to identify potential improvements. Even if your ideas have been considered before, demonstrating initiative shows engagement and ambition.

  • Share your work: Engineers often hesitate to highlight their accomplishments, but appropriate visibility helps the team understand your strengths and interests. Use team meetings as opportunities to showcase completed work.

  • Mentor others: If there are newer team members or junior engineers, start paying forward the help you’ve received. This cements your own understanding while building your reputation as a team player. Remember you were new ones too, and how did that feel?

  • Set long-term goals: Discuss career development with your manager, establishing clear objectives for the next 6-12 months. This might include specific technical skills to develop or projects you’d like to lead.

  • Optimize workflows: Look for opportunities to improve efficiency through automation, process refinement, or tech stack enhancements. As a data engineer, you might identify ways to optimize pipelines, improve data quality, or reduce cloud compute costs. This is a particularly effective way to demonstrate measurable impact through savings calculations.



Setting the Tone for Success

Your first 90 days represent a unique window of opportunity. You have the advantage of fresh eyes combined with gradually developing context—a powerful combination for identifying improvements while still building credibility.

Focusing on the “Last Mile” of Data

As you progress through these phases, remember that data engineering isn’t just about building pipelines—it’s about ensuring data actually drives decisions. This “last mile” of data delivery is often where many teams fall short. Consider how you can help with:

  • Evangelization: Raising awareness that quality data exists and is available
  • Persuasion: Showing stakeholders how this data will improve their decision-making
  • Usability: Creating clear documentation, example queries, and interfaces that match user needs
  • Expectation setting: Ensuring users understand what the data can and cannot do

Each stakeholder group—executives, data scientists, ML engineers, other data engineers, and software engineers—has unique needs. Learning to tailor your approach accordingly will dramatically increase your impact.

The framework I’ve outlined provides structure without being overly prescriptive. Adapt it to your specific situation, taking into account team size, company maturity, and role expectations. The key is having a deliberate approach rather than simply reacting to whatever comes your way.

Remember that this period is as much about learning as it is about contributing. By balancing curiosity with initiative and relationship-building with technical exploration, you’ll establish yourself as both competent and collaborative—setting the foundation for long-term success in your role.

Whether you’re a junior engineer in your first position or a seasoned professional in a new environment, approaching these critical first months with intention and structure will significantly increase your chances of making a positive, lasting impact.



Practical 30-60-90 Day Checklist

First 30 Days Checklist

  • Set up 1:1s with all immediate team members
  • Schedule at least three skip-level meetings with senior leaders
  • Request a comprehensive codebase walkthrough
  • Read all available documentation and identify gaps
  • Create a map of key system components and data flows
  • Fix at least two simple bugs
  • Learn the team’s development workflow and tooling
  • Understand the current data architecture and pipeline design
  • Identify the team’s biggest pain points (ask explicitly in 1:1s)
  • Document your own onboarding experience and questions
  • Map out who the key decision-makers are that consume your team’s data
  • Identify what type of impact (measurable, hard-to-measure, immeasurable) is most valued

60 Days Checklist

  • Lead a design discussion for a feature or component
  • Provide substantive feedback in at least five code reviews
  • Update or create documentation for an undocumented area
  • Take ownership of a moderately complex feature or improvement
  • Schedule a progress review with your manager
  • Understand the business context and objectives driving technical decisions
  • Map relationships between your team’s work and broader company goals
  • Identify at least one process that could be improved
  • Contribute to the team’s technical roadmap discussions
  • Measure and track your own productivity and impact

90 Days Checklist

  • Propose a significant improvement or new initiative
  • Present your work to the broader team or department
  • Mentor a newer team member or contribute to knowledge sharing
  • Establish long-term professional development goals with your manager
  • Identify and implement at least one efficiency improvement
  • Take full ownership of a critical system or component
  • Contribute to hiring or interviewing (if applicable)
  • Build relationships with stakeholders outside your immediate team
  • Reflect on your progress and adjust your approach as needed
  • Set objectives for your next three months
  • Document a measurable impact you’ve had (cost savings, performance improvement)
  • Create or improve at least one data interface to make insights more accessible
  • Understand what different stakeholders need from your data (executives vs. engineers)

By approaching your first 90 days with this structured methodology, you’ll not only accelerate your own integration but also clearly demonstrate your value to the organization. Remember that the goal isn’t to check every box perfectly—it’s to establish momentum and patterns that will serve you throughout your tenure.